Branch Length Estimates Research Articles

L1-Tree: A novel algorithm for constructing 3D tree models and estimating branch architectural traits using terrestrial laser scanning data

Branch architecture provides crucial information for the understanding of plant trait variability and the adaptive strategies employed by trees in response to their environment. High-fidelity terrestrial laser scanning (TLS) data provide an accurate, efficient, and non-destructive means for constructing three-dimensional (3D) tree models and estimating architectural traits. However, the complex canopy structure of trees in natural forests and the presence of occlusion in TLS data pose significant challenges to achieving this goal. In this study, we present a novel algorithm, L1-Tree, for the construction of 3D tree models and the estimation of architectural traits from TLS data. This algorithm is grounded in the L1-Median algorithm and integrates a tree skeleton optimization procedure that considers the structural characteristics of tree branches. By comparing modeling results and manually derived branch traits for 24 trees of 24 species, we found that the L1-Tree algorithm achieved precision, recall, and F-score values of 0.94 for branch identification, coefficient of determination, root-mean-squared error, and normalized root-mean-squared error of 0.998, 0.068 m, and 0.3 % for branch length estimation, and a respective value of 0.958, 0.257 cm and 0.9 % for branch radius estimation. Additionally, the branch identification accuracy and accuracy in branch architectural trait estimation remained satisfactory across branch orders. Compared to established 3D tree model construction algorithms (e.g., TreeQSM), our L1-Tree algorithm demonstrated a superior capability in handling noisy environments and data gaps, making it a robust tool for TLS data-based tree architecture studies. Leaf-wood separation emerged as a crucial step influencing the performance of the L1-Tree algorithm. We observed significant drop in branch identification accuracy when using an automatic leaf-wood separation algorithm as input, highlighting the urgent need to develop effective leaf-wood separation algorithms to generate high-quality wood point clouds for tree architecture studies.

Reliable estimation of tree branch lengths using deep neural networks.

A phylogenetic tree represents hypothesized evolutionary history for a set of taxa. Besides the branching patterns (i.e., tree topology), phylogenies contain information about the evolutionary distances (i.e. branch lengths) between all taxa in the tree, which include extant taxa (external nodes) and their last common ancestors (internal nodes). During phylogenetic tree inference, the branch lengths are typically co-estimated along with other phylogenetic parameters during tree topology space exploration. There are well-known regions of the branch length parameter space where accurate estimation of phylogenetic trees is especially difficult. Several novel studies have recently demonstrated that machine learning approaches have the potential to help solve phylogenetic problems with greater accuracy and computational efficiency. In this study, as a proof of concept, we sought to explore the possibility of machine learning models to predict branch lengths. To that end, we designed several deep learning frameworks to estimate branch lengths on fixed tree topologies from multiple sequence alignments or its representations. Our results show that deep learning methods can exhibit superior performance in some difficult regions of branch length parameter space. For example, in contrast to maximum likelihood inference, which is typically used for estimating branch lengths, deep learning methods are more efficient and accurate. In general, we find that our neural networks achieve similar accuracy to a Bayesian approach and are the best-performing methods when inferring long branches that are associated with distantly related taxa. Together, our findings represent a next step toward accurate, fast, and reliable phylogenetic inference with machine learning approaches.

Maximum likelihood phylogeographic inference of cell motility and cell division from spatial lineage tracing data.

Recently developed spatial lineage tracing technologies induce somatic mutations at specific genomic loci in a population of growing cells and then measure these mutations in the sampled cells along with the physical locations of the cells. These technologies enable high-throughput studies of developmental processes over space and time. However, these applications rely on accurate reconstruction of a spatial cell lineage tree describing both past cell divisions and cell locations. Spatial lineage trees are related to phylogeographic models that have been well-studied in the phylogenetics literature. We demonstrate that standard phylogeographic models based on Brownian motion are inadequate to describe the spatial symmetric displacement (SD) of cells during cell division. We introduce a new model-the SD model for cell motility that includes symmetric displacements of daughter cells from the parental cell followed by independent diffusion of daughter cells. We show that this model more accurately describes the locations of cells in a real spatial lineage tracing of mouse embryonic stem cells. Combining the spatial SD model with an evolutionary model of DNA mutations, we obtain a phylogeographic model for spatial lineage tracing. Using this model, we devise a maximum likelihood framework-MOLLUSC (Maximum Likelihood Estimation Of Lineage and Location Using Single-Cell Spatial Lineage tracing Data)-to co-estimate time-resolved branch lengths, spatial diffusion rate, and mutation rate. On both simulated and real data, we show that MOLLUSC accurately estimates all parameters. In contrast, the Brownian motion model overestimates spatial diffusion rate in all test cases. In addition, the inclusion of spatial information improves accuracy of branch length estimation compared to sequence data alone. On real data, we show that spatial information has more signal than sequence data for branch length estimation, suggesting augmenting lineage tracing technologies with spatial information is useful to overcome the limitations of genome-editing in developmental systems. The python implementation of MOLLUSC is available at https://github.com/raphael-group/MOLLUSC.

Toward a Semi-Supervised Learning Approach to Phylogenetic Estimation.

Models have always been central to inferring molecular evolution and to reconstructing phylogenetic trees. Their use typically involves the development of a mechanistic framework reflecting our understanding of the underlying biological processes, such as nucleotide substitutions, and the estimation of model parameters by maximum likelihood or Bayesian inference. However, deriving and optimizing the likelihood of the data is not always possible under complex evolutionary scenarios or even tractable for large datasets, often leading to unrealistic simplifying assumptions in the fitted models. To overcome this issue, we coupled stochastic simulations of genome evolution with a new supervised deep-learning model to infer key parameters of molecular evolution. Our model is designed to directly analyze multiple sequence alignments and estimate per-site evolutionary rates and divergence without requiring a known phylogenetic tree. The accuracy of our predictions matched that of likelihood-based phylogenetic inference when rate heterogeneity followed a simple gamma distribution, but it strongly exceeded it under more complex patterns of rate variation, such as codon models. Our approach is highly scalable and can be efficiently applied to genomic data, as we showed on a dataset of 26 million nucleotides from the clownfish clade. Our simulations also showed that the integration of per-site rates obtained by deep learning within a Bayesian framework led to significantly more accurate phylogenetic inference, particularly with respect to the estimated branch lengths. We thus propose that future advancements in phylogenetic analysis will benefit from a semi-supervised learning approach that combines deep-learning estimation of substitution rates, which allows for more flexible models of rate variation, and probabilistic inference of the phylogenetic tree, which guarantees interpretability and a rigorous assessment of statistical support.

Sequential Bayesian Phylogenetic Inference.

The ideal approach to Bayesian phylogenetic inference is to estimate all parameters of interest jointly in a single hierarchical model. However, this is often not feasible in practice due to the high computational cost. Instead, phylogenetic pipelines generally consist of sequential analyses, whereby a single point estimate from a given analysis is used as input for the next analysis (e.g., a single multiple sequence alignment is used to estimate a gene tree). In this framework, uncertainty is not propagated from step to step, which can lead to inaccurate or spuriously confident results. Here, we formally develop and test a sequential inference approach for Bayesian phylogenetic inference, which uses importance sampling to generate observations for the next step of an analysis pipeline from the posterior distribution produced in the previous step. Our sequential inference approach presented here not only accounts for uncertainty between analysis steps, but also allows for greater flexibility in software choice (and hence model availability) and can be computationally more efficient than the traditional joint inference approach when multiple models are being tested. We show that our sequential inference approach is identical in practice to the joint inference approach only if sufficient information in the data is present (a narrow posterior distribution) and/or sufficiently many importance samples are used. Conversely, we show that the common practice of using a single point estimate can be biased, e.g., a single phylogeny estimate to transform an unrooted phylogeny into a time-calibrated phylogeny. We demonstrate the theory of sequential Bayesian inference using both a toy example and an empirical case study of divergence-time estimation in insects using a relaxed clock model from transcriptome data. In the empirical example, we estimate three posterior distributions of branch lengths from the same data (DNA character matrix with a GTR+Γ+I substitution model, an amino acid data matrix with empirical substitution models, and an amino acid data matrix with the PhyloBayes CAT-GTR model). Finally, we apply three different node-calibration strategies and show that divergence-time estimates are affected by both the data source and underlying substitution process to estimate branch lengths as well as the node-calibration strategies. Thus, our new sequential Bayesian phylogenetic inference provides the opportunity to efficiently test different approaches for divergence time estimation, including branch-length estimation from other software.

Gene flow and an anomaly zone complicate phylogenomic inference in a rapidly radiated avian family (Prunellidae)

BackgroundResolving the phylogeny of rapidly radiating lineages presents a challenge when building the Tree of Life. An Old World avian family Prunellidae (Accentors) comprises twelve species that rapidly diversified at the Pliocene–Pleistocene boundary.ResultsHere we investigate the phylogenetic relationships of all species of Prunellidae using a chromosome-level de novo assembly of Prunella strophiata and 36 high-coverage resequenced genomes. We use homologous alignments of thousands of exonic and intronic loci to build the coalescent and concatenated phylogenies and recover four different species trees. Topology tests show a large degree of gene tree-species tree discordance but only 40–54% of intronic gene trees and 36–75% of exonic genic trees can be explained by incomplete lineage sorting and gene tree estimation errors. Estimated branch lengths for three successive internal branches in the inferred species trees suggest the existence of an empirical anomaly zone. The most common topology recovered for species in this anomaly zone was not similar to any coalescent or concatenated inference phylogenies, suggesting presence of anomalous gene trees. However, this interpretation is complicated by the presence of gene flow because extensive introgression was detected among these species. When exploring tree topology distributions, introgression, and regional variation in recombination rate, we find that many autosomal regions contain signatures of introgression and thus may mislead phylogenetic inference. Conversely, the phylogenetic signal is concentrated to regions with low-recombination rate, such as the Z chromosome, which are also more resistant to interspecific introgression.ConclusionsCollectively, our results suggest that phylogenomic inference should consider the underlying genomic architecture to maximize the consistency of phylogenomic signal.

Phylogenomic branch length estimation using quartets.

Branch lengths and topology of a species tree are essential in most downstream analyses, including estimation of diversification dates, characterization of selection, understanding adaptation, and comparative genomics. Modern phylogenomic analyses often use methods that account for the heterogeneity of evolutionary histories across the genome due to processes such as incomplete lineage sorting. However, these methods typically do not generate branch lengths in units that are usable by downstream applications, forcing phylogenomic analyses to resort to alternative shortcuts such as estimating branch lengths by concatenating gene alignments into a supermatrix. Yet, concatenation and other available approaches for estimating branch lengths fail to address heterogeneity across the genome. In this article, we derive expected values of gene tree branch lengths in substitution units under an extension of the multispecies coalescent (MSC) model that allows substitutions with varying rates across the species tree. We present CASTLES, a new technique for estimating branch lengths on the species tree from estimated gene trees that uses these expected values, and our study shows that CASTLES improves on the most accurate prior methods with respect to both speed and accuracy. CASTLES is available at https://github.com/ytabatabaee/CASTLES.

Using all Gene Families Vastly Expands Data Available for Phylogenomic Inference.

Traditionally, single-copy orthologs have been the gold standard in phylogenomics. Most phylogenomic studies identify putative single-copy orthologs using clustering approaches and retain families with a single sequence per species. This limits the amount of data available by excluding larger families. Recent advances have suggested several ways to include data from larger families. For instance, tree-based decomposition methods facilitate the extraction of orthologs from large families. Additionally, several methods for species tree inference are robust to the inclusion of paralogs and could use all of the data from larger families. Here, we explore the effects of using all families for phylogenetic inference by examining relationships among 26 primate species in detail and by analyzing five additional data sets. We compare single-copy families, orthologs extracted using tree-based decomposition approaches, and all families with all data. We explore several species tree inference methods, finding that identical trees are returned across nearly all subsets of the data and methods for primates. The relationships among Platyrrhini remain contentious; however, the species tree inference method matters more than the subset of data used. Using data from larger gene families drastically increases the number of genes available and leads to consistent estimates of branch lengths, nodal certainty and concordance, and inferences of introgression in primates. For the other data sets, topological inferences are consistent whether single-copy families or orthologs extracted using decomposition approaches are analyzed. Using larger gene families is a promising approach to include more data in phylogenomics without sacrificing accuracy, at least when high-quality genomes are available.

SNP-based phylogenomic inference in Holarctic ground squirrels (Urocitellus)

Resolution of rapid evolutionary radiatons requires harvesting maximal signal from phylogenomic datasets. However, studies of non-model clades often target conserved loci that are characterized by reduced information content, which can negatively affect gene tree precision and species tree accuracy. Single nucleotide polymorphism (SNP)-based methods are an underutilized but potentially valuable tool for estimating phylogeny and divergence times because they do not rely on resolved gene trees, allowing information from many or all variant loci to be leveraged in species tree reconstruction. We evaluated the utility of SNP-based methods in resolving phylogeny of Holarctic ground squirrels (Urocitellus), a radiation that has been difficult to disentangle, even in prior phylogenomic studies. We inferred phylogeny from a dataset of >3,000 ultraconserved element loci (UCEs) using two methods (SNAPP, SVDquartets) and compared our results with a new mitogenome phylogeny. We also systematically evaluated how phasing of UCEs improves per-locus information content, inference of topology, and other parameters within each of these SNP-based methods. Phasing improved topological resolution and branch length estimation at shallow levels (within species complexes), but less so at deeper levels, likely reflecting true uncertainty due to ancestral polymorphisms segregating in rapidly diverging lineages. We resolved key clades in Urocitellus and present targeted opportunities for future phylogenomic inquiry. Our results also extend the roadmap for use of SNPs to address vertebrate radiations and inform comparative analyses at multiple temporal scales.

Theoretical and Practical Considerations when using Retroelement Insertions to Estimate Species Trees in the Anomaly Zone.

A potential shortcoming of concatenation methods for species tree estimation is their failure to account for incomplete lineage sorting. Coalescent methods address this problem but make various assumptions that, if violated, can result in worse performance than concatenation. Given the challenges of analyzing DNA sequences with both concatenation and coalescent methods, retroelement insertions (RIs) have emerged as powerful phylogenomic markers for species tree estimation. Here, we show that two recently proposed quartet-based methods, SDPquartets and ASTRAL_BP, are statistically consistent estimators of the unrooted species tree topology under the coalescent when RIs follow a neutral infinite-sites model of mutation and the expected number of new RIs per generation is constant across the species tree. The accuracy of these (and other) methods for inferring species trees from RIs has yet to be assessed on simulated data sets, where the true species tree topology is known. Therefore, we evaluated eight methods given RIs simulated from four model species trees, all of which have short branches and at least three of which are in the anomaly zone. In our simulation study, ASTRAL_BP and SDPquartets always recovered the correct species tree topology when given a sufficiently large number of RIs, as predicted. A distance-based method (ASTRID_BP) and Dollo parsimony also performed well in recovering the species tree topology. In contrast, unordered, polymorphism, and Camin-Sokal parsimony (as well as an approach based on MDC) typically fail to recover the correct species tree topology in anomaly zone situations with more than four ingroup taxa. Of the methods studied, only ASTRAL_BP automatically estimates internal branch lengths (in coalescent units) and support values (i.e., local posterior probabilities). We examined the accuracy of branch length estimation, finding that estimated lengths were accurate for short branches but upwardly biased otherwise. This led us to derive the maximum likelihood (branch length) estimate for when RIs are given as input instead of binary gene trees; this corrected formula produced accurate estimates of branch lengths in our simulation study provided that a sufficiently large number of RIs were given as input. Lastly, we evaluated the impact of data quantity on species tree estimation by repeating the above experiments with input sizes varying from 100 to 100,000 parsimony-informative RIs. We found that, when given just 1000 parsimony-informative RIs as input, ASTRAL_BP successfully reconstructed major clades (i.e., clades separated by branches $>0.3$ coalescent units) with high support and identified rapid radiations (i.e., shorter connected branches), although not their precise branching order. The local posterior probability was effective for controlling false positive branches in these scenarios. [Coalescence; incomplete lineage sorting; Laurasiatheria; Palaeognathae; parsimony; polymorphism parsimony; retroelement insertions; species trees; transposon.].

Concordance-Based Approaches for the Inference of Relationships and Molecular Rates with Phylogenomic Data Sets.

Gene tree conflict is common and finding methods to analyze and alleviate the negative effects that conflict has on species tree analysis is a crucial part of phylogenomics. This study aims to expand the discussion of inferring species trees and molecular branch lengths when conflict is present. Conflict is typically examined in two ways: inferring its prevalence and inferring the influence of the individual genes (how strongly one gene supports any given topology compared to an alternative topology). Here, we examine a procedure for incorporating both conflict and the influence of genes in order to infer evolutionary relationships. All supported relationships in the gene trees are analyzed and the likelihood of the genes constrained to these relationships is summed to provide a likelihood for the relationship. Consensus tree assembly is conducted based on the sum of likelihoods for a given relationship and choosing relationships based on the most likely relationship assuming it does not conflict with a relationship that has a higher likelihood score. If it is not possible for all most likely relationships to be combined into a single bifurcating tree then multiple trees are produced and a consensus tree with a polytomy is created. This procedure allows for more influential genes to have a greater influence on an inferred relationship, does not assume conflict has arisen from any one source and does not force the data set to produce a single bifurcating tree. Using this approach, on three empirical data sets, we examine and discuss the relationship between influence and prevalence of gene tree conflict. We find that in one of the data sets, assembling a bifurcating consensus tree solely composed of the most likely relationships is impossible. To account for conflict in molecular rate analysis we also introduce a concordance-based approach to the summary and estimation of branch lengths suitable for downstream comparative analyses. We demonstrate through simulation that even under high levels of stochastic conflict, the mean and median of the concordant rates recapitulate the true molecular rate better than using a supermatrix approach. Using a large phylogenomic data set, we examine rate heterogeneity across concordant genes with a focus on the branch subtending crown angiosperms. Notably, we find highly variable rates of evolution along the branch subtending crown angiosperms. The approaches outlined here have several limitations, but they also represent some alternative methods for harnessing the complexity of phylogenomic data sets and enrich our inferences of both species relationships and evolutionary processes.[Branch length estimation; consensus tree; gene tree conflict; gene tree filtering; phylogenetics; phylogenomics.].

A fast likelihood approach for estimation of large phylogenies from continuous trait data

Despite the recent availability of large-scale genomic data for many individuals, few methods for phylogenetic inference are both computationally efficient and highly accurate for trees with hundreds of taxa. Model-based methods such as those developed in the maximum likelihood and Bayesian frameworks are especially time-consuming, as they involve both computationally intensive calculations on fixed phylogenies and searches through the space of possible phylogenies, and they are known to scale poorly with the addition of taxa. Here, we propose a fast approximation to the maximum likelihood estimator that directly uses continuous trait data, such as allele frequency data. The approximation works by first computing the maximum likelihood estimates of some internal branch lengths, and then inferring the tree-topology using these estimates. Our approach is more computationally efficient than existing methods for such data while still achieving comparable accuracy. This method is innovative in its use of the mathematical properties of tree-topologies for inference, and thus serves as a useful addition to the collection of methods available for estimating phylogenies from continuous trait data.

Patterns of variation of mutation rates of mitochondrial and nuclear genes of gastropods

BackgroundAlthough mitochondrial DNA (mtDNA) of many animals tends to mutate at higher rates than nuclear DNA (nuDNA), a recent survey of mutation rates of various animal groups found that the gastropod family Bradybaenidae (suborder Helicina) shows a nearly 40-fold difference in mutation rates of mtDNA (mum) and nuDNA (mun), while other gastropod taxa exhibit only two to five-fold differences. To determine if Bradybaenidae represents an outlier within Gastropoda, I compared estimated values of mum/mun of additional gastropod groups. In particular, I reconstructed mtDNA and nuDNA gene trees of 121 datasets that include members of various clades contained within the gastropod subclasses Caenogastropoda, Heterobranchia, Patellogastropoda, and Vetigastropoda and then used total branch length estimates of these gene trees to infer mum/mun.ResultsEstimated values of mum/mun range from 1.4 to 91.9. Datasets that exhibit relatively large values of mum/mun (i.e., > 20), however, show relatively lower estimates of mun (and not elevated mum) in comparison to groups with lower values. These datasets also tend to contain sequences of recently diverged species. In addition, datasets with low levels of phylogenetic breadth (i.e., contain members of single genera or families) exhibit higher values of mum/mun than those with high levels (i.e., those that contain representatives of single superfamilies or higher taxonomic ranks).ConclusionsGastropods exhibit considerable variation in estimates of mum/mun. Large values of mum/mun that have been calculated for Bradybaenidae and other gastropod taxa may be overestimated due to possible sampling artifacts or processes that depress estimates of total molecular divergence of nuDNA in groups that recently diversified.

PRANC: ML species tree estimation from the ranked gene trees under coalescence.

PRANC computes the Probabilities of RANked gene tree topologies under the multispecies coalescent. A ranked gene tree is a gene tree accounting for the temporal ordering of internal nodes. PRANC can also estimate the maximum likelihood (ML) species tree from a sample of ranked or unranked gene tree topologies. It estimates the ML tree with estimated branch lengths in coalescent units. PRANC is written in C++ and freely available at github.com/anastasiiakim/PRANC. Supplementary data are available at Bioinformatics online.

ModelTeller: Model Selection for Optimal Phylogenetic Reconstruction Using Machine Learning.

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.

Modeling methylation dynamics with simultaneous changes in CpG islands

BackgroundIn vertebrate genomes, CpG sites can be clustered into CpG islands, and the amount of methylation in a CpG island can change due to gene regulation processes. Thus, single regulatory events can simultaneously change the methylation states of many CpG sites within a CpG island. This should be taken into account when quantifying the amount of change in methylation, for example in form of a branch length in a phylogeny of cell types.ResultsWe propose a probabilistic model (the IWE-SSE model) of methylation dynamics that accounts for simultaneous methylation changes in multiple CpG sites belonging to the same CpG island. We further propose a Markov-chain Monte-Carlo (MCMC) method to fit this model to methylation data from cell type phylogenies and apply this method to available data from murine haematopoietic cells and from human cell lines. Combined with simulation studies, these analyses show that accounting for CpG island wide methylation changes has a strong effect on the inferred branch lengths and leads to a significantly better model fit for the methylation data from murine haematopoietic cells and human cell lines.ConclusionThe MCMC based parameter estimation method for the IWE-SSE model in combination with our MCMC based inference method allows to quantify the amount of methylation changes at single CpG sites as well as on entire CpG islands. Accounting for changes affecting entire islands can lead to more accurate branch length estimation in the presence of simultaneous methylation change.

Whole genome phylogeny of Gallus: introgression and data-type effects

BackgroundPrevious phylogenetic studies that include the four recognized species of Gallus have resulted in a number of distinct topologies, with little agreement. Several factors could lead to the failure to converge on a consistent topology, including introgression, incomplete lineage sorting, different data types, or insufficient data.MethodsWe generated three novel whole genome assemblies for Gallus species, which we combined with data from the published genomes of Gallus gallus and Bambusicola thoracicus (a member of the sister genus to Gallus). To determine why previous studies have failed to converge on a single topology, we extracted large numbers of orthologous exons, introns, ultra-conserved elements, and conserved non-exonic elements from the genome assemblies. This provided more than 32 million base pairs of data that we used for concatenated maximum likelihood and multispecies coalescent analyses of Gallus.ResultsAll of our analyses, regardless of data type, yielded a single, well-supported topology. We found some evidence for ancient introgression involving specific Gallus lineages as well as modest data type effects that had an impact on support and branch length estimates in specific analyses. However, the estimated gene tree spectra for all data types had a relatively good fit to their expectation given the multispecies coalescent.ConclusionsOverall, our data suggest that conflicts among previous studies probably reflect the use of smaller datasets (both in terms of number of sites and of loci) in those analyses. Our results demonstrate the importance of sampling large numbers of loci, each of which has a sufficient number of sites to provide robust estimates of gene trees. Low-coverage whole genome sequencing, as we did here, represents a cost-effective means to generate the very large data sets that include multiple data types that enabled us to obtain a robust estimate of Gallus phylogeny.

Challenges in estimating ancestral state reconstructions: the evolution of migration in Sylvia warblers as a study case.

Our current understanding of how species have evolved is mainly based on comparative phylogenetic methods, which use phylogenies to infer the evolution of traits. The development of ancestral state reconstruction (ASR) methods has provided the tools to reconstruct trait evolution, which are widely used in fields like evolutionary biology, macroecology and paleontology. As there are different elements involved in those analyses, with different levels of uncertainty (i.e. relating to branch length estimation, trait coding, statistical framework, taxon sampling or software), the various combinations of these elements likely have a strong impact on the reconstruction of the evolution of traits, potentially leading to opposite conclusions. To assess the impact of these different elements in ASR, we performed a set of analyses, including all possible combinations of such elements and using the evolution of migratory behavior in Sylvia warblers as a case study, which was coded as a continuous or as a discrete character. Our results show that taxon sampling, character coding, tree shape, statistical framework and software all significantly affect ASR, both individually and in combination. Not all reconstructed tree nodes show discrepancies, but in the critical ones most pairwise comparisons between analyses lead to conflicting and unexpectedly antagonistic results (zero migration vs fully migratory), thus challenging interpretations of trait evolution. We propose some possible solutions to partly inform decisions, involving the method selection and the incorporation of biological or fossil evidence regarding how traits evolve, but our results demand serious rethinking about how the research community currently uses ASR.

A High-Quality Reference Genome Assembly of the Saltwater Crocodile, Crocodylus porosus, Reveals Patterns of Selection in Crocodylidae.

Crocodilians are an economically, culturally, and biologically important group. To improve researchers’ ability to study genome structure, evolution, and gene regulation in the clade, we generated a high-quality de novo genome assembly of the saltwater crocodile, Crocodylus porosus, from Illumina short read data from genomic libraries and in vitro proximity-ligation libraries. The assembled genome is 2,123.5 Mb, with N50 scaffold size of 17.7 Mb and N90 scaffold size of 3.8 Mb. We then annotated this new assembly, increasing the number of annotated genes by 74%. In total, 96% of 23,242 annotated genes were associated with a functional protein domain. Furthermore, multiple noncoding functional regions and mappable genetic markers were identified. Upon analysis and overlapping the results of branch length estimation and site selection tests for detecting potential selection, we found 16 putative genes under positive selection in crocodilians, 10 in C. porosus and 6 in Alligator mississippiensis. The annotated C. porosus genome will serve as an important platform for osmoregulatory, physiological, and sex determination studies, as well as an important reference in investigating the phylogenetic relationships of crocodilians, birds, and other tetrapods.

A method for genome-wide genealogy estimation for thousands of samples

Knowledge of genome-wide genealogies for thousands of individuals would simplify most evolutionary analyses for humans and other species, but has remained computationally infeasible. We have developed a method, Relate, scaling to >10,000 sequences while simultaneously estimating branch lengths, mutational ages and variable historical population sizes, as well as allowing for data errors. Application to 1,000 Genomes Project haplotypes produces joint genealogical histories for 26 human populations. Highly diverged lineages are present in all groups, but most frequent in Africa. Outside Africa, these mainly reflect ancient introgression from groups related to Neanderthals and Denisovans, while African signals instead reflect unknown events unique to that continent. Our approach allows more powerful inferences of natural selection than has previously been possible. We identify multiple regions under strong positive selection, and multi-allelic traits including hair color, body mass index and blood pressure, showing strong evidence of directional selection, varying among human groups.