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Changes in chromosome numbers are a prominent driver of plant evolution, impacting ecological diversification, stress tolerance, and phenotypes. ChromEvol is a widely used software tool for deciphering patterns of chromosome-number change along a phylogeny of interest. It evaluates the fit of alternative models to the data, estimates transition rates of different types of events, and infers the expected number of events along each branch of the phylogeny. We introduce ChromEvol v.3, featuring multiple novel methodological advancements that capture variation in the transition rates along a phylogeny. This version better allows researchers to identify how dysploidy and polyploidy rates change based on the number of chromosomes in the genome, with respect to a discrete trait, or at certain subclades of the phylogeny. We demonstrate the applicability of the new models on the Solanaceae phylogeny. Our analyses identify four chromosome-number transition regimes that characterize distinct Solanaceae clades and demonstrate an association between self-compatibility and altered dynamics of chromosome-number evolution. ChromEvol v.3, available at https://github.com/anatshafir1/chromevol, offers researchers a more flexible, comprehensive, and accurate tool to investigate the evolution of chromosome numbers and the various processes affecting it., (© 2024 The Author(s). New Phytologist © 2024 New Phytologist Foundation.)

Introns are highly prevalent in most eukaryotic genomes. Despite the accumulating evidence for benefits conferred by the possession of introns, their specific roles and functions, as well as the processes shaping their evolution, are still only partially understood. Here, we explore the evolution of the eukaryotic intron-exon gene structure by focusing on several key features such as the intron length, the number of introns, and the intron-to-exon length ratio in protein-coding genes. We utilize whole-genome data from 590 species covering the main eukaryotic taxonomic groups and analyze them within a statistical phylogenetic framework. We found that the basic gene structure differs markedly among the main eukaryotic groups, with animals, and particularly chordates, displaying intron-rich genes, compared with plants and fungi. Reconstruction of gene structure evolution suggests that these differences evolved prior to the divergence of the main phyla and have remained mostly conserved within groups. We revisit the previously reported association between the genome size and the mean intron length and report that this association differs considerably among phyla. Analyzing a large and diverse dataset of species with whole-genome information while applying advanced modeling techniques allowed us to obtain a global evolutionary perspective. Our findings may indicate that introns play different molecular and evolutionary roles in different organisms., (© The Author(s) 2024. Published by Oxford University Press on behalf of Society for Molecular Biology and Evolution.)

Motivation: Currently used methods for estimating branch support in phylogenetic analyses often rely on the classic Felsenstein's bootstrap, parametric tests, or their approximations. As these branch support scores are widely used in phylogenetic analyses, having accurate, fast, and interpretable scores is of high importance., Results: Here, we employed a data-driven approach to estimate branch support values with a probabilistic interpretation. To this end, we simulated thousands of realistic phylogenetic trees and the corresponding multiple sequence alignments. Each of the obtained alignments was used to infer the phylogeny using state-of-the-art phylogenetic inference software, which was then compared to the true tree. Using these extensive data, we trained machine-learning algorithms to estimate branch support values for each bipartition within the maximum-likelihood trees obtained by each software. Our results demonstrate that our model provides fast and more accurate probability-based branch support values than commonly used procedures. We demonstrate the applicability of our approach on empirical datasets., Availability and Implementation: The data supporting this work are available in the Figshare repository at https://doi.org/10.6084/m9.figshare.25050554.v1, and the underlying code is accessible via GitHub at https://github.com/noaeker/bootstrap_repo., (© The Author(s) 2024. Published by Oxford University Press.)

The computational search for the maximum-likelihood phylogenetic tree is an NP-hard problem. As such, current tree search algorithms might result in a tree that is the local optima, not the global one. Here, we introduce a paradigm shift for predicting the maximum-likelihood tree, by approximating long-term gains of likelihood rather than maximizing likelihood gain at each step of the search. Our proposed approach harnesses the power of reinforcement learning to learn an optimal search strategy, aiming at the global optimum of the search space. We show that when analyzing empirical data containing dozens of sequences, the log-likelihood improvement from the starting tree obtained by the reinforcement learning-based agent was 0.969 or higher compared to that achieved by current state-of-the-art techniques. Notably, this performance is attained without the need to perform costly likelihood optimizations apart from the training process, thus potentially allowing for an exponential increase in runtime. We exemplify this for data sets containing 15 sequences of length 18,000 bp and demonstrate that the reinforcement learning-based method is roughly three times faster than the state-of-the-art software. This study illustrates the potential of reinforcement learning in addressing the challenges of phylogenetic tree reconstruction., (© The Author(s) 2024. Published by Oxford University Press on behalf of Society for Molecular Biology and Evolution.)

Motivation: Insertions and deletions (indels) of short DNA segments, along with substitutions, are the most frequent molecular evolutionary events. Indels were shown to affect numerous macro-evolutionary processes. Because indels may span multiple positions, their impact is a product of both their rate and their length distribution. An accurate inference of indel-length distribution is important for multiple evolutionary and bioinformatics applications, most notably for alignment software. Previous studies counted the number of continuous gap characters in alignments to determine the best-fitting length distribution. However, gap-counting methods are not statistically rigorous, as gap blocks are not synonymous with indels. Furthermore, such methods rely on alignments that regularly contain errors and are biased due to the assumption of alignment methods that indels lengths follow a geometric distribution., Results: We aimed to determine which indel-length distribution best characterizes alignments using statistical rigorous methodologies. To this end, we reduced the alignment bias using a machine-learning algorithm and applied an Approximate Bayesian Computation methodology for model selection. Moreover, we developed a novel method to test if current indel models provide an adequate representation of the evolutionary process. We found that the best-fitting model varies among alignments, with a Zipf length distribution fitting the vast majority of them., Availability and Implementation: The data underlying this article are available in Github, at https://github.com/elyawy/SpartaSim and https://github.com/elyawy/SpartaPipeline., (© The Author(s) 2024. Published by Oxford University Press.)

There are two main life cycles in plants-annual and perennial 1,2 . These life cycles are associated with different traits that determine ecosystem function 3,4 . Although life cycles are textbook examples of plant adaptation to different environments, we lack comprehensive knowledge regarding their global distributional patterns. Here we assembled an extensive database of plant life cycle assignments of 235,000 plant species coupled with millions of georeferenced datapoints to map the worldwide biogeography of these plant species. We found that annual plants are half as common as initially thought 5-8 , accounting for only 6% of plant species. Our analyses indicate that annuals are favoured in hot and dry regions. However, a more accurate model shows that the prevalence of annual species is driven by temperature and precipitation in the driest quarter (rather than yearly means), explaining, for example, why some Mediterranean systems have more annuals than desert systems. Furthermore, this pattern remains consistent among different families, indicating convergent evolution. Finally, we demonstrate that increasing climate variability and anthropogenic disturbance increase annual favourability. Considering future climate change, we predict an increase in annual prevalence for 69% of the world's ecoregions by 2060. Overall, our analyses raise concerns for ecosystem services provided by perennial plants, as ongoing changes are leading to a higher proportion of annual plants globally., (© 2023. The Author(s), under exclusive licence to Springer Nature Limited.)

Pan-genomics is an emerging approach for studying the genetic diversity within plant populations. In contrast to common resequencing studies that compare whole genome sequencing data with a single reference genome, the construction of a pan-genome (PG) involves the direct comparison of multiple genomes to one another, thereby enabling the detection of genomic sequences and genes not present in the reference, as well as the analysis of gene content diversity. Although multiple studies describing PGs of various plant species have been published in recent years, a better understanding regarding the effect of the computational procedures used for PG construction could guide researchers in making more informed methodological decisions. Here, we examine the effect of several key methodological factors on the obtained gene pool and on gene presence-absence detections by constructing and comparing multiple PGs of Arabidopsis thaliana and cultivated soybean, as well as conducting a meta-analysis on published PGs. These factors include the construction method, the sequencing depth, and the extent of input data used for gene annotation. We observe substantial differences between PGs constructed using three common procedures (de novo assembly and annotation, map-to-pan, and iterative assembly) and that results are dependent on the extent of the input data. Specifically, we report low agreement between the gene content inferred using different procedures and input data. Our results should increase the awareness of the community to the consequences of methodological decisions made during the process of PG construction and emphasize the need for further investigation of commonly applied methodologies., (© The Author(s) 2023. Published by Oxford University Press on behalf of Society for Molecular Biology and Evolution.)

Changes in chromosome numbers, including polyploidy and dysploidy events, play a key role in eukaryote evolution as they could expediate reproductive isolation and have the potential to foster phenotypic diversification. Deciphering the pattern of chromosome-number change within a phylogeny currently relies on probabilistic evolutionary models. All currently available models assume time homogeneity, such that the transition rates are identical throughout the phylogeny. Here, we develop heterogeneous models of chromosome-number evolution that allow multiple transition regimes to operate in distinct parts of the phylogeny. The partition of the phylogeny to distinct transition regimes may be specified by the researcher or, alternatively, identified using a sequential testing approach. Once the number and locations of shifts in the transition pattern are determined, a second search phase identifies regimes with similar transition dynamics, which could indicate on convergent evolution. Using simulations, we study the performance of the developed model to detect shifts in patterns of chromosome-number evolution and demonstrate its applicability by analyzing the evolution of chromosome numbers within the Cyperaceae plant family. The developed model extends the capabilities of probabilistic models of chromosome-number evolution and should be particularly helpful for the analyses of large phylogenies that include multiple distinct subclades., (© 2023 The Authors. New Phytologist © 2023 New Phytologist Foundation.)

Plant genomes are characterized by large and complex gene families that often result in similar and partially overlapping functions. This genetic redundancy severely hampers current efforts to uncover novel phenotypes, delaying basic genetic research and breeding programmes. Here we describe the development and validation of Multi-Knock, a genome-scale clustered regularly interspaced short palindromic repeat toolbox that overcomes functional redundancy in Arabidopsis by simultaneously targeting multiple gene-family members, thus identifying genetically hidden components. We computationally designed 59,129 optimal single-guide RNAs that each target two to ten genes within a family at once. Furthermore, partitioning the library into ten sublibraries directed towards a different functional group allows flexible and targeted genetic screens. From the 5,635 single-guide RNAs targeting the plant transportome, we generated over 3,500 independent Arabidopsis lines that allowed us to identify and characterize the first known cytokinin tonoplast-localized transporters in plants. With the ability to overcome functional redundancy in plants at the genome-scale level, the developed strategy can be readily deployed by scientists and breeders for basic research and to expedite breeding efforts., (© 2023. The Author(s), under exclusive licence to Springer Nature Limited.)

The ConSurf web-sever for the analysis of proteins, RNA, and DNA provides a quick and accurate estimate of the per-site evolutionary rate among homologues. The analysis reveals functionally important regions, such as catalytic and ligand-binding sites, which often evolve slowly. Since the last report in 2016, ConSurf has been improved in multiple ways. It now has a user-friendly interface that makes it easier to perform the analysis and to visualize the results. Evolutionary rates are calculated based on a set of homologous sequences, collected using hidden Markov model-based search tools, recently embedded in the pipeline. Using these, and following the removal of redundancy, ConSurf assembles a representative set of effective homologues for protein and nucleic acid queries to enable informative analysis of the evolutionary patterns. The analysis is particularly insightful when the evolutionary rates are mapped on the macromolecule structure. In this respect, the availability of AlphaFold model structures of essentially all UniProt proteins makes ConSurf particularly relevant to the research community. The UniProt ID of a query protein with an available AlphaFold model can now be used to start a calculation. Another important improvement is the Python re-implementation of the entire computational pipeline, making it easier to maintain. This Python pipeline is now available for download as a standalone version. We demonstrate some of ConSurf's key capabilities by the analysis of caveolin-1, the main protein of membrane invaginations called caveolae., (© 2023 The Authors. Protein Science published by Wiley Periodicals LLC on behalf of The Protein Society.)

The ChromEvol software was the first to implement a likelihood-based approach, using probabilistic models that depict the pattern of chromosome number change along a specified phylogeny. The initial models have been completed and expanded during the last years. New parameters that model polyploid chromosome evolution have been implemented in ChromEvol v.2. In recent years, new and more complex models have been developed. The BiChrom model is able to implement two distinct chromosome models for the two possible trait states of a binary character of interest. ChromoSSE jointly implements chromosome evolution, speciation, and extinction. In the near future, we will be able to study chromosome evolution with increasingly complex models., (© 2023. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Chromosome numbers have long been used for the identification of key genomic events such as polyploidy and dysploidy. These inferences are often challenging, particularly when applied to large phylogenies, or clades in which more than a few chromosome number transitions had occurred. Here we describe the chromEvol computational framework that infers shifts in chromosome numbers along a phylogeny using probabilistic models of chromosome number change. Given chromosome count data and an associated phylogeny, chromEvol identifies such patterns by fitting probabilistic models of chromosome number evolution to the data. We describe the chromEvol workflow using available online tools, including the specification of the desired models, the examination of model fit to the data, and the inference of ploidy levels. The pipeline can be used by the wide scientific community and requires no previous computational or programming skills., (© 2023. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

For decades, plant biologists have been interested in the determination and documentation of chromosome numbers for extant taxa. This central cytological character has been used as an important phylogenetic marker and as an indicator for major genomic events such as polyploidy and dysploidy. Due to their significance and the relative ease by which chromosome numbers can be obtained, chromosome numbers have been extensively recorded across the plant kingdom and documented in a wide variety of resources. This makes the collection process a wearing task, often leading to partial data retrieval. In 2015, the Chromosome Counts Database (CCDB) was assembled, being an online unified community resource. This database compiles dozens of different chromosome counts sources, of which a significant portion had been unavailable before in a digitized, searchable format. The vast amount of data assembled in CCDB has already enabled a large number of analyses to examine the evolution of different plant hierarchies, as well as the application of various follow-up analyses, such as ploidy-level inference using chromEvol. CCDB ( http://ccdb.tau.ac.il/ ) encourages data sharing among the botanical community and is expected to continue expanding as additional chromosome numbers are recorded., (© 2023. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Insertions and deletions (indels) of short DNA segments are common evolutionary events. Numerous studies showed that deletions occur more often than insertions in both prokaryotes and eukaryotes. It raises the question why neutral sequences are not eradicated from the genome. We suggest that this is due to a phenomenon we term border-induced selection . Accordingly, a neutral sequence is bordered between conserved regions. Deletions occurring near the borders occasionally protrude to the conserved region and are thereby subject to strong purifying selection. Thus, for short neutral sequences, an insertion bias is expected. Here, we develop a set of increasingly complex models of indel dynamics that incorporate border-induced selection. Furthermore, we show that short conserved sequences within the neutrally evolving sequence help explain: (i) the presence of very long sequences; (ii) the high variance of sequence lengths; and (iii) the possible emergence of multimodality in sequence length distributions. Finally, we fitted our models to the human intron length distribution, as introns are thought to be mostly neutral and bordered by conserved exons. We show that when accounting for the occurrence of short conserved sequences within introns, we reproduce the main features, including the presence of long introns and the multimodality of intron distribution.

Motivation: In recent years, full-genome sequences have become increasingly available and as a result many modern phylogenetic analyses are based on very long sequences, often with over 100 000 sites. Phylogenetic reconstructions of large-scale alignments are challenging for likelihood-based phylogenetic inference programs and usually require using a powerful computer cluster. Current tools for alignment trimming prior to phylogenetic analysis do not promise a significant reduction in the alignment size and are claimed to have a negative effect on the accuracy of the obtained tree., Results: Here, we propose an artificial-intelligence-based approach, which provides means to select the optimal subset of sites and a formula by which one can compute the log-likelihood of the entire data based on this subset. Our approach is based on training a regularized Lasso-regression model that optimizes the log-likelihood prediction accuracy while putting a constraint on the number of sites used for the approximation. We show that computing the likelihood based on 5% of the sites already provides accurate approximation of the tree likelihood based on the entire data. Furthermore, we show that using this Lasso-based approximation during a tree search decreased running-time substantially while retaining the same tree-search performance., Availability and Implementation: The code was implemented in Python version 3.8 and is available through GitHub (https://github.com/noaeker/lasso_positions_sampling). The datasets used in this paper were retrieved from Zhou et al. (2018) as described in section 3., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2022. Published by Oxford University Press.)

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., (© The Author(s) 2021. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.)

The role of plant-pollinator interactions in the rapid radiation of the angiosperms have long fascinated evolutionary biologists. Studies have brought evidence for pollinator-driven diversification of various plant lineages, particularly plants with specialized flowers and concealed rewards. By contrast, little is known about how this crucial interaction has shaped macroevolutionary patterns of floral visitors. In particular, there is currently no empirical evidence that floral host association has increased diversification in bees, the most prominent group of floral visitors that essentially rely on angiosperm pollen. In this study, we examine how floral host preference influenced diversification in eucerine bees (Apidae, Eucerini), which exhibit large variations in their floral associations. We combine quantitative pollen analyses with a recently proposed phylogenetic hypothesis, and use a state speciation and extinction probabilistic approach. Using this framework, we provide the first evidence that multiple evolutionary transitions from host plants with accessible pollen to restricted pollen from 'bee-flowers' have significantly increased the diversification of a bee clade. We suggest that exploiting host plants with restricted pollen has allowed the exploitation of a new ecological niche for eucerine bees and contributed both to their colonization of vast regions of the world and their rapid diversification.

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.

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., (© 2021 John Wiley & Sons Ltd.)

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 Author(s) 2020. Published by Oxford University Press, on behalf of the Society of Systematic Biologists. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Inferring a phylogenetic tree is a fundamental challenge in evolutionary studies. Current paradigms for phylogenetic tree reconstruction rely on performing costly likelihood optimizations. With the aim of making tree inference feasible for problems involving more than a handful of sequences, inference under the maximum-likelihood paradigm integrates heuristic approaches to evaluate only a subset of all potential trees. Consequently, existing methods suffer from the known tradeoff between accuracy and running time. In this proof-of-concept study, we train a machine-learning algorithm over an extensive cohort of empirical data to predict the neighboring trees that increase the likelihood, without actually computing their likelihood. This provides means to safely discard a large set of the search space, thus potentially accelerating heuristic tree searches without losing accuracy. Our analyses suggest that machine learning can guide tree-search methodologies towards the most promising candidate trees.

Chromosome number is a central feature of eukaryote genomes. Deciphering patterns of chromosome-number change along a phylogeny is central to the inference of whole genome duplications and ancestral chromosome numbers. ChromEvol is a probabilistic inference tool that allows the evaluation of several models of chromosome-number evolution and their fit to the data. However, fitting a model does not necessarily mean that the model describes the empirical data adequately. This vulnerability may lead to incorrect conclusions when model assumptions are not met by real data. Here, we present a model adequacy test for likelihood models of chromosome-number evolution. The procedure allows us to determine whether the model can generate data with similar characteristics as those found in the observed ones. We demonstrate that using inadequate models can lead to inflated errors in several inference tasks. Applying the developed method to 200 angiosperm genera, we find that in many of these, the best-fitting model provides poor fit to the data. The inadequacy rate increases in large clades or in those in which hybridizations are present. The developed model adequacy test can help researchers to identify phylogenies whose underlying evolutionary patterns deviate substantially from current modelling assumptions and should guide future methods development., (© 2020 The Authors New Phytologist © 2020 New Phytologist Foundation.)

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., (© The Author(s) 2020. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.)

The rapid spread of SARS-CoV-2 and its threat to health systems worldwide have led governments to take acute actions to enforce social distancing. Previous studies used complex epidemiological models to quantify the effect of lockdown policies on infection rates. However, these rely on prior assumptions or on official regulations. Here, we use country-specific reports of daily mobility from people cellular usage to model social distancing. Our data-driven model enabled the extraction of lockdown characteristics which were crossed with observed mortality rates to show that: (i) the time at which social distancing was initiated is highly correlated with the number of deaths, r 2  = 0.64, while the lockdown strictness or its duration is not as informative; (ii) a delay of 7.49 days in initiating social distancing would double the number of deaths; and (iii) the immediate response has a prolonged effect on COVID-19 death toll., (© 2020 The Authors. Published under the terms of the CC BY 4.0 license.)

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., (© The Author(s) 2020. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

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., (© 2020 The Authors. Evolution © 2020 The Society for the Study of Evolution.)

Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives., (© 2019 The Authors. Global Change Biology published by John Wiley & Sons Ltd.)

If particular traits consistently affect rates of speciation and extinction, broad macroevolutionary patterns can be interpreted as consequences of selection at high levels of the biological hierarchy. Identifying traits associated with diversification rates is difficult because of the wide variety of characters under consideration and the statistical challenges of testing for associations from comparative phylogenetic data. Ploidy (diploid vs polyploid states) and breeding system (self-incompatible vs self-compatible states) are both thought to be drivers of differential diversification in angiosperms. We fit 29 diversification models to extensive trait and phylogenetic data in Solanaceae and investigate how speciation and extinction rate differences are associated with ploidy, breeding system, and the interaction between these traits. We show that diversification patterns in Solanaceae are better explained by breeding system and an additional unobserved factor, rather than by ploidy. We also find that the most common evolutionary pathway to polyploidy in Solanaceae occurs via direct breakdown of self-incompatibility by whole genome duplication, rather than indirectly via breakdown followed by polyploidization. Comparing multiple stochastic diversification models that include complex trait interactions alongside hidden states enhances our understanding of the macroevolutionary patterns in plant phylogenies., (© 2019 The Authors. New Phytologist © 2019 New Phytologist Trust.)

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., (2019 The Authors Evolution published by Wiley Periodicals, Inc. on behalf of The Society for the Study of Evolution.)

Determining the most suitable model for phylogeny reconstruction constitutes a fundamental step in numerous evolutionary studies. Over the years, various criteria for model selection have been proposed, leading to debate over which criterion is preferable. However, the necessity of this procedure has not been questioned to date. Here, we demonstrate that although incongruency regarding the selected model is frequent over empirical and simulated data, all criteria lead to very similar inferences. When topologies and ancestral sequence reconstruction are the desired output, choosing one criterion over another is not crucial. Moreover, skipping model selection and using instead the most parameter-rich model, GTR+I+G, leads to similar inferences, thus rendering this time-consuming step nonessential, at least under current strategies of model selection.

Deciphering the global distribution of polyploid plants is fundamental for understanding plant evolution and ecology. Many factors have been hypothesized to affect the uneven distribution of polyploid plants across the globe. Nevertheless, the lack of large comparative datasets has restricted such studies to local floras and to narrow taxonomical scopes, limiting our understanding of the underlying drivers of polyploid plant distribution. We present a map portraying the worldwide polyploid frequencies, based on extensive spatial data coupled with phylogeny-based polyploidy inference for tens of thousands of species. This allowed us to assess the potential global drivers affecting polyploid distribution. Our data reveal a clear latitudinal trend, with polyploid frequency increasing away from the equator. Climate, especially temperature, appears to be the most influential predictor of polyploid distribution. However, we find this effect to be mostly indirect, mediated predominantly by variation in plant lifeforms and, to a lesser extent, by taxonomical composition and species richness. Thus, our study presents an emerging view of polyploid distribution that highlights attributes that facilitate the establishment of new polyploid lineages by providing polyploids with sufficient time (that is, perenniality) and space (low species richness) to compete with pre-adapted diploid relatives.

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., (© 2018 John Wiley & Sons Ltd.)

Cation/proton antiporters (CPAs) play a major role in maintaining living cells' homeostasis. CPAs are commonly divided into two main groups, CPA1 and CPA2, and are further characterized by two main phenotypes: ion selectivity and electrogenicity. However, tracing the evolutionary relationships of these transporters is challenging because of the high diversity within CPAs. Here, we conduct comprehensive evolutionary analysis of 6537 representative CPAs, describing the full complexity of their phylogeny, and revealing a sequence motif that appears to determine central phenotypic characteristics. In contrast to previous suggestions, we show that the CPA1/CPA2 division only partially correlates with electrogenicity. Our analysis further indicates two acidic residues in the binding site that carry the protons in electrogenic CPAs, and a polar residue in the unwound transmembrane helix 4 that determines ion selectivity. A rationally designed triple mutant successfully converted the electrogenic CPA, EcNhaA, to be electroneutral.

The development of the CRISPR-Cas9 system in recent years has made eukaryotic genome editing, and specifically gene knockout for reverse genetics, a simple and effective task. The system is directed to a genomic target site by a programmed single-guide RNA (sgRNA) that base-pairs with it, subsequently leading to site-specific modifications. However, many gene families in eukaryotic genomes exhibit partially overlapping functions, and thus, the knockout of one gene might be concealed by the function of the other. In such cases, the reduced specificity of the CRISPR-Cas9 system, which may lead to the modification of genomic sites that are not identical to the sgRNA, can be harnessed for the simultaneous knockout of multiple homologous genes. We introduce CRISPys, an algorithm for the optimal design of sgRNAs that would potentially target multiple members of a given gene family. CRISPys first clusters all the potential targets in the input sequences into a hierarchical tree structure that specifies the similarity among them. Then, sgRNAs are proposed in the internal nodes of the tree by embedding mismatches where needed, such that the efficiency to edit the induced targets is maximized. We suggest several approaches for designing the optimal individual sgRNA and an approach to compute the optimal set of sgRNAs for cases when the experimental platform allows for more than one. The latter may optionally account for the homologous relationships among gene-family members. We further show that CRISPys outperforms simpler alignment-based techniques by in silico examination over all gene families in the Solanum lycopersicum genome., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Species diversity is remarkably unevenly distributed among flowering plant lineages. Despite a growing toolbox of research methods, the reasons underlying this patchy pattern have continued to perplex plant biologists for the past two decades. In this review, we examine the present understanding of transitions in flowering plant evolution that have been proposed to influence speciation and extinction. In particular, ploidy changes, transitions between tropical and nontropical biomes, and shifts in floral form have received attention and have offered some surprises in terms of which factors influence speciation and extinction rates. Mating systems and dispersal characteristics once predominated as determining factors, yet recent evidence suggests that these changes are not as influential as previously thought or are important only when paired with range shifts. Although range extent is an important correlate of speciation, it also influences extinction and brings an applied focus to diversification research. Recent studies that find that past diversification can predict present-day extinction risk open an exciting avenue for future research to help guide conservation prioritization.

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.]., (© The Author(s) 2017. Published by Oxford University Press, on behalf of the Society of Systematic Biologists. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

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 postpolyploid 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 intertribal 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 northeastern 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 2.1-3.75-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 multispeed 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., (© 2017 John Wiley & Sons Ltd.)

The adaptation of the CRISPR-Cas9 system as a genome editing technique has generated much excitement in recent years owing to its ability to manipulate targeted genes and genomic regions that are complementary to a programmed single guide RNA (sgRNA). However, the efficacy of a specific sgRNA is not uniquely defined by exact sequence homology to the target site, thus unintended off-targets might additionally be cleaved. Current methods for sgRNA design are mainly concerned with predicting off-targets for a given sgRNA using basic sequence features and employ elementary rules for ranking possible sgRNAs. Here, we introduce CRISTA (CRISPR Target Assessment), a novel algorithm within the machine learning framework that determines the propensity of a genomic site to be cleaved by a given sgRNA. We show that the predictions made with CRISTA are more accurate than other available methodologies. We further demonstrate that the occurrence of bulges is not a rare phenomenon and should be accounted for in the prediction process. Beyond predicting cleavage efficiencies, the learning process provides inferences regarding patterns that underlie the mechanism of action of the CRISPR-Cas9 system. We discover that attributes that describe the spatial structure and rigidity of the entire genomic site as well as those surrounding the PAM region are a major component of the prediction capabilities.

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., (© The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.)

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., (© 2017 The Author(s). Evolution © 2017 The Society for the Study of Evolution.)

[This corrects the article DOI: 10.1002/ece3.2277.].

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., (© The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.)

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., (© 2016 Botanical Society of America.)

Premise of the Study: Polyploidization is a common and recurring phenomenon in plants and is often thought to be a mechanism of "instant speciation". Whether polyploidization is associated with the formation of new species (cladogenesis) or simply occurs over time within a lineage (anagenesis), however, has never been assessed systematically., Methods: We tested this hypothesis using phylogenetic and karyotypic information from 235 plant genera (mostly angiosperms). We first constructed a large database of combined sequence and chromosome number data sets using an automated procedure. We then applied likelihood models (ClaSSE) that estimate the degree of synchronization between polyploidization and speciation events in maximum likelihood and Bayesian frameworks., Key Results: Our maximum likelihood analysis indicated that 35 genera supported a model that includes cladogenetic transitions over a model with only anagenetic transitions, whereas three genera supported a model that incorporates anagenetic transitions over one with only cladogenetic transitions. Furthermore, the Bayesian analysis supported a preponderance of cladogenetic change in four genera but did not support a preponderance of anagenetic change in any genus., Conclusions: Overall, these phylogenetic analyses provide the first broad confirmation that polyploidization is temporally associated with speciation events, suggesting that it is indeed a major speciation mechanism in plants, at least in some genera., (© 2016 Botanical Society of America.)

Vertebrate sex-determining mechanisms (SDMs) are triggered by the genotype (GSD), by temperature (TSD), or occasionally, by both. The causes and consequences of SDM diversity remain enigmatic. Theory predicts SDM effects on species diversification, and life-span effects on SDM evolutionary turnover. Yet, evidence is conflicting in clades with labile SDMs, such as reptiles. Here, we investigate whether SDM is associated with diversification in turtles and lizards, and whether alterative factors, such as lifespan's effect on transition rates, could explain the relative prevalence of SDMs in turtles and lizards (including and excluding snakes). We assembled a comprehensive dataset of SDM states for squamates and turtles and leveraged large phylogenies for these two groups. We found no evidence that SDMs affect turtle, squamate, or lizard diversification. However, SDM transition rates differ between groups. In lizards TSD-to-GSD surpass GSD-to-TSD transitions, explaining the predominance of GSD lizards in nature. SDM transitions are fewer in turtles and the rates are similar to each other (TSD-to-GSD equals GSD-to-TSD), which, coupled with TSD ancestry, could explain TSD's predominance in turtles. These contrasting patterns can be explained by differences in life history. Namely, our data support the notion that in general, shorter lizard lifespan renders TSD detrimental favoring GSD evolution in squamates, whereas turtle longevity permits TSD retention. Thus, based on the macro-evolutionary evidence we uncovered, we hypothesize that turtles and lizards followed different evolutionary trajectories with respect to SDM, likely mediated by differences in lifespan. Combined, our findings revealed a complex evolutionary interplay between SDMs and life histories that warrants further research that should make use of expanded datasets on unexamined taxa to enable more conclusive analyses.

Dioecy, the sexual system in which male and female organs are found in separate individuals, allows greater specialization for sex-specific functions and can be advantageous under various ecological and environmental conditions. However, dioecy is rare among flowering plants. Previous studies identified contradictory trends regarding the relative diversification rates of dioecious lineages vs their nondioecious counterparts, depending on the methods and data used. We gathered detailed species-level data for dozens of genera that contain both dioecious and nondioecious species. We then applied a probabilistic approach that accounts for differential speciation, extinction, and transition rates between states to examine whether there is an association between dioecy and lineage diversification. We found a bimodal distribution, whereby dioecious lineages exhibited higher diversification in certain genera but lower diversification in others. Additional analyses did not uncover an ecological or life history trait that could explain a context-dependent effect of dioecy on diversification. Furthermore, in-depth simulations of neutral characters demonstrated that such bimodality is also found when simulating neutral characters across the observed trees. Our analyses suggest that - at least for these genera with the currently available data - dioecy neither consistently places a strong brake on diversification nor is a strong driver., (© 2015 The Authors. New Phytologist © 2015 New Phytologist Trust.)

Alternative splicing is a key cellular mechanism for generating distinct isoforms, whose relative abundances regulate critical cellular processes. It is therefore essential that inclusion levels of alternative exons be tightly regulated. However, how the precision of inclusion levels among individual cells is governed is poorly understood. Using single-cell gene expression, we show that the precision of inclusion levels of alternative exons is determined by the degree of evolutionary conservation at their flanking intronic regions. Moreover, the inclusion levels of alternative exons, as well as the expression levels of the transcripts harboring them, also contribute to this precision. We further show that alternative exons whose inclusion levels are considerably changed during stem cell differentiation are also subject to this regulation. Our results imply that alternative splicing is coordinately regulated to achieve accuracy in relative isoform abundances and that such accuracy may be important in determining cell fate., (© 2015 The Authors. Published under the terms of the CC BY 4.0 license.)