Incomplete lineage sorting and phenotypic evolution in marsupials.

Incomplete lineage sorting (ILS) makes ancestral genetic polymorphisms persist during rapid speciation events, inducing incongruences between gene trees and species trees. ILS has complicated phylogenetic inference in many lineages, including hominids. However, we lack empirical evidence that ILS leads to incongruent phenotypic variation. Here, we performed phylogenomic analyses to show that the South American monito del monte is the sister lineage of all Australian marsupials, although over 31% of its genome is closer to the Diprotodontia than to other Australian groups due to ILS during ancient radiation. Pervasive conflicting phylogenetic signals across the whole genome are consistent with some of the morphological variation among extant marsupials. We detected hundreds of genes that experienced stochastic fixation during ILS, encoding the same amino acids in non-sister species. Using functional experiments, we confirm how ILS may have directly contributed to hemiplasy in morphological traits that were established during rapid marsupial speciation ca. 60 mya.

Quantitative fate mapping: A general framework for analyzing progenitor state dynamics via retrospective lineage barcoding.

Natural and induced somatic mutations that accumulate in the genome during development record the phylogenetic relationships of cells; whether these lineage barcodes capture the complex dynamics of progenitor states remains unclear. We introduce quantitative fate mapping, an approach to reconstruct the hierarchy, commitment times, population sizes, and commitment biases of intermediate progenitor states during development based on a time-scaled phylogeny of their descendants. To reconstruct time-scaled phylogenies from lineage barcodes, we introduce Phylotime, a scalable maximum likelihood clustering approach based on a general barcoding mutagenesis model. We validate these approaches using realistic in silico and in vitro barcoding experiments. We further establish criteria for the number of cells that must be analyzed for robust quantitative fate mapping and a progenitor state coverage statistic to assess the robustness. This work demonstrates how lineage barcodes, natural or synthetic, enable analyzing progenitor fate and dynamics long after embryonic development in any organism.

PhyloBench: A Benchmark for Evaluating Phylogenetic Programs.

Phylogenetic inference based on protein sequence alignment is a widely used procedure. Numerous phylogenetic algorithms have been developed, most of which have many parameters and options. Choosing a program, options, and parameters can be a nontrivial task. No benchmark for comparison of phylogenetic programs on real protein sequences was publicly available. We have developed PhyloBench, a benchmark for evaluating the quality of phylogenetic inference, and used it to test a number of popular phylogenetic programs. PhyloBench is based on natural, not simulated, protein sequences of orthologous evolutionary domains. The measure of accuracy of an inferred tree is its distance to the corresponding species tree. A number of tree-to-tree distance measures were tested. The most reliable results were obtained using the Robinson-Foulds distance. Our results confirmed recent findings that distance methods are more accurate than maximum likelihood (ML) and maximum parsimony. We tested the bayesian program MrBayes on natural protein sequences and found that, on our datasets, it performs better than ML, but worse than distance methods. Of the methods we tested, the Balanced Minimum Evolution method implemented in FastME yielded the best results on our material. Alignments and reference species trees are available at https://mouse.belozersky.msu.ru/tools/phylobench/ together with a web-interface that allows for a semi-automatic comparison of a user's method with a number of popular programs.

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 substitu- tions, 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 accu- rate 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.

Shared single copy genes are generally reliable for inferring phylogenetic relationships among polyploid taxa.

Polyploidy, or whole-genome duplication, is expected to confound the inference of species trees with phylogenetic methods for two reasons. First, the presence of retained duplicated genes requires the reconciliation of the inferred gene trees to a proposed species tree. Second, even if the analyses are restricted to shared single copy genes, the occurrence of reciprocal gene loss, where the surviving genes in different species are paralogs from the polyploidy rather than orthologs, will mean that such genes will not have evolved under the corresponding species tree and may not produce gene trees that allow inference of that species tree. Here we analyze three different ancient polyploidy events, using synteny-based inferences of orthology and paralogy to infer gene trees from nearly 17,000 sets of homologous genes. We find that the simple use of single copy genes from polyploid organisms provides reasonably robust phylogenetic signals, despite the presence of reciprocal gene losses. Such gene trees are also most often in accord with the inferred species relationships inferred from maximum likelihood models of gene loss after polyploidy: a completely distinct phylogenetic signal present in these genomes. As seen in other studies, however, we find that methods for inferring phylogenetic confidence yield high support values even in cases where the underlying data suggest meaningful conflict in the phylogenetic signals.

The ClaDS rate-heterogeneous birth-death prior for full phylogenetic inference in BEAST2.

Bayesian phylogenetic inference requires a tree prior, which models the underlying diversification process that gives rise to the phylogeny. Existing birth-death diversification models include a wide range of features, for instance, lineage-specific variations in speciation and extinction (SSE) rates. While across-lineage variation in SSE rates is widespread in empirical datasets, few heterogeneous rate models have been implemented as tree priors for Bayesian phylogenetic inference. As a consequence, rate heterogeneity is typically ignored when reconstructing phylogenies, and rate heterogeneity is usually investigated on fixed trees. In this paper, we present a new BEAST2 package implementing the cladogenetic diversification rate shift (ClaDS) model as a tree prior. ClaDS is a birth-death diversification model designed to capture small progressive variations in birth and death rates along a phylogeny. Unlike previous implementations of ClaDS, which were designed to be used with fixed, user-chosen phylogenies, our package is implemented in the BEAST2 framework and thus allows full phylogenetic inference, where the phylogeny and model parameters are co-estimated from a molecular alignment. Our package provides all necessary components of the inference, including a new tree object and operators to propose moves to the Monte-Carlo Markov chain. It also includes a graphical interface through BEAUti. We validate our implementation of the package by comparing the produced distributions to simulated data and show an empirical example of the full inference, using a dataset of cetaceans.

Distance-based phylogenetic inference from typing data: a unifying view.

Typing methods are widely used in the surveillance of infectious diseases, outbreaks investigation and studies of the natural history of an infection. Moreover, their use is becoming standard, in particular with the introduction of high-throughput sequencing. On the other hand, the data being generated are massive and many algorithms have been proposed for a phylogenetic analysis of typing data, addressing both correctness and scalability issues. Most of the distance-based algorithms for inferring phylogenetic trees follow the closest pair joining scheme. This is one of the approaches used in hierarchical clustering. Moreover, although phylogenetic inference algorithms may seem rather different, the main difference among them resides on how one defines cluster proximity and on which optimization criterion is used. Both cluster proximity and optimization criteria rely often on a model of evolution. In this work, we review, and we provide a unified view of these algorithms. This is an important step not only to better understand such algorithms but also to identify possible computational bottlenecks and improvements, important to deal with large data sets.

On the effects of selection and mutation on species tree inference.

The effect of selection acting on regions of the genome on the accuracy of species-level phylogenetic inference using methods that do not explicitly model selection is an open question that is relevant to most, if not all, phylogenomic studies. To address this, we derive a mathematical approximation to the Wright-Fisher model with mutation and selection in the limit as the population size becomes large. In contrast to previous approximations based on diffusion processes, our approximation can be used to study the distribution of coalescent times for an arbitrary number of lineages, allowing calculation of the probability distribution of gene genealogies under the coalescent model. We use these calculations to show that direct selection at strengths typically encountered in practice has only a small effect on the distribution of coalescent times, and hence on the distribution of gene trees. This implies that many coalescent-based methods for estimating the species tree topology will be robust to the presence of selection in a subset of the underlying genes. Selection will, however, bias the estimation of speciation times, causing them to underestimate the true speciation times. Our model captures the effects of selection on the genealogies that generate the observed sequence data, but does not model selective pressures that act only on the subsequent sequences or that negatively impact gene tree estimation.

Genetic characteristics of a novel HIV-1 recombinant lineage (CRF103_01B) and its prevalence in northern China.

During the routine surveillance of HIV-1 pretreatment drug resistance in Beijing, five men who have sex with men (MSM) and a woman were observed to get infected by newly identified CRF103_01B strain. To elucidate the genetic characteristics, the near full-length genome (NFLG) was obtained. Phylogenetic inference indicated that CRF103_01B NFLG was composed of six mosaic segments. Segments IV and V of CRF103_01B were located among the clusters subtype B and CRF01_AE (group 5), respectively. The CRF103_01B strain was deduced to originate from Beijing MSM population around 2002.3-2006.4 and continued to spread among MSM population at a low level, then to the general population via heterosexual contact in northern China. Molecular epidemiology surveillance of CRF103_01B should be reinforced.

Representing and extending ensembles of parsimonious evolutionary histories with a directed acyclic graph.

In many situations, it would be useful to know not just the best phylogenetic tree for a given data set, but the collection of high-quality trees. This goal is typically addressed using Bayesian techniques, however, current Bayesian methods do not scale to large data sets. Furthermore, for large data sets with relatively low signal one cannot even store every good tree individually, especially when the trees are required to be bifurcating. In this paper, we develop a novel object called the "history subpartition directed acyclic graph" (or "history sDAG" for short) that compactly represents an ensemble of trees with labels (e.g. ancestral sequences) mapped onto the internal nodes. The history sDAG can be built efficiently and can also be efficiently trimmed to only represent maximally parsimonious trees. We show that the history sDAG allows us to find many additional equally parsimonious trees, extending combinatorially beyond the ensemble used to construct it. We argue that this object could be useful as the "skeleton" of a more complete uncertainty quantification.

Phylogeny of Alisma (Alismataceae) revisited: implications for polyploid evolution and species delimitation.

Alisma L. is a genus of aquatic and wetland plants belonging to family Alismataceae. At present, it is thought to contain ten species. Variation in ploidy level is known in the genus, with diploids, tetraploids and hexaploids recorded. Previous molecular phylogenetic studies of Alisma have generated a robust backbone that reveals important aspects of the evolutionary history of this cosmopolitan genus, yet questions remain unresolved about the formation of the polyploid taxa and the taxonomy of one particularly challenging, widely distributed species complex. Here we directly sequenced, or cloned and sequenced, nuclear DNA (nrITS and phyA) and chloroplast DNA (matK, ndhF, psbA-trnH and rbcL) of multiple samples of six putative species and two varieties, and conducted molecular phylogenetic analyses. Alisma canaliculatum and its two varieties known in East Asia and A. rariflorum endemic to Japan possess closely related but heterogeneous genomes, strongly indicating that the two species were generated from two diploid progenitors, and are possibly siblings of one another. This evolutionary event may have occurred in Japan. Alisma canaliculatum var. canaliculatum is segregated into two types, each of which are geographically slightly differentiated in Japan. We reconstructed a single phylogeny based on the multi-locus data using Homologizer and then applied species delimitation analysis (STACEY). This allowed us to discern A. orientale as apparently endemic to the Southeast Asian Massif and distinct from the widespread A. plantago-aquatica. The former species was most likely formed through parapatric speciation at the southern edge of the distribution of the latter.

Phylogenomic Subsampling and the Search for Phylogenetically Reliable Loci.

Phylogenomic subsampling is a procedure by which small sets of loci are selected from large genome-scale data sets and used for phylogenetic inference. This step is often motivated by either computational limitations associated with the use of complex inference methods or as a means of testing the robustness of phylogenetic results by discarding loci that are deemed potentially misleading. Although many alternative methods of phylogenomic subsampling have been proposed, little effort has gone into comparing their behavior across different data sets. Here, I calculate multiple gene properties for a range of phylogenomic data sets spanning animal, fungal, and plant clades, uncovering a remarkable predictability in their patterns of covariance. I also show how these patterns provide a means for ordering loci by both their rate of evolution and their relative phylogenetic usefulness. This method of retrieving phylogenetically useful loci is found to be among the top performing when compared with alternative subsampling protocols. Relatively common approaches such as minimizing potential sources of systematic bias or increasing the clock-likeness of the data are found to fare worse than selecting loci at random. Likewise, the general utility of rate-based subsampling is found to be limited: loci evolving at both low and high rates are among the least effective, and even those evolving at optimal rates can still widely differ in usefulness. This study shows that many common subsampling approaches introduce unintended effects in off-target gene properties and proposes an alternative multivariate method that simultaneously optimizes phylogenetic signal while controlling for known sources of bias.

Phylogenetic Analysis of SARS-CoV-2 Data Is Difficult.

Numerous studies covering some aspects of SARS-CoV-2 data analyses are being published on a daily basis, including a regularly updated phylogeny on nextstrain.org. Here, we review the difficulties of inferring reliable phylogenies by example of a data snapshot comprising a quality-filtered subset of 8,736 out of all 16,453 virus sequences available on May 5, 2020 from gisaid.org. We find that it is difficult to infer a reliable phylogeny on these data due to the large number of sequences in conjunction with the low number of mutations. We further find that rooting the inferred phylogeny with some degree of confidence either via the bat and pangolin outgroups or by applying novel computational methods on the ingroup phylogeny does not appear to be credible. Finally, an automatic classification of the current sequences into subclasses using the mPTP tool for molecular species delimitation is also, as might be expected, not possible, as the sequences are too closely related. We conclude that, although the application of phylogenetic methods to disentangle the evolution and spread of COVID-19 provides some insight, results of phylogenetic analyses, in particular those conducted under the default settings of current phylogenetic inference tools, as well as downstream analyses on the inferred phylogenies, should be considered and interpreted with extreme caution.

How Often Does Filtering of Alignment Columns Improve the Phylogenetic Inference of Two-Domain Proteins?

ae-mail: sas@belozersky.msu.ru Protein phylogeny is usually reconstructed basing on a multiple alignment of amino acid sequences. One of the problems of such alignments is the presence of regions with different degree of conservation, including those with a questionable quality of the alignment. This problem is often solved by filtering the alignment columns with a special software developed for this purpose. In this work, we investigated various approaches to the phylogeny reconstruction using proteins with two evolutionary domains as examples. The sequences of such proteins are inherently heterogeneous in the degree of conservation due to the presence of both evolutionary domains and linkers between them, as well as the N- and C-termini. It is shown that filtering the alignment columns on average improves the quality of reconstruction only when using the full-length sequences and only for eukaryotic proteins. Limiting the alignment to the evolutionary domains with rejection of less conserved linkers and terminal sequences on average worsened the quality of phylogenetic reconstruction.

Identifiability of species network topologies from genomic sequences using the logDet distance.

Inference of network-like evolutionary relationships between species from genomic data must address the interwoven signals from both gene flow and incomplete lineage sorting. The heavy computational demands of standard approaches to this problem severely limit the size of datasets that may be analyzed, in both the number of species and the number of genetic loci. Here we provide a theoretical pointer to more efficient methods, by showing that logDet distances computed from genomic-scale sequences retain sufficient information to recover network relationships in the level-1 ultrametric case. This result is obtained under the Network Multispecies Coalescent model combined with a mixture of General Time-Reversible sequence evolution models across individual gene trees. It applies to both unlinked site data, such as for SNPs, and to sequence data in which many contiguous sites may have evolved on a common tree, such as concatenated gene sequences. Thus under standard stochastic models statistically justifiable inference of network relationships from sequences can be accomplished without consideration of individual genes or gene trees.

New Methods to Calculate Concordance Factors for Phylogenomic Datasets.

We implement two measures for quantifying genealogical concordance in phylogenomic data sets: the gene concordance factor (gCF) and the novel site concordance factor (sCF). For every branch of a reference tree, gCF is defined as the percentage of "decisive" gene trees containing that branch. This measure is already in wide usage, but here we introduce a package that calculates it while accounting for variable taxon coverage among gene trees. sCF is a new measure defined as the percentage of decisive sites supporting a branch in the reference tree. gCF and sCF complement classical measures of branch support in phylogenetics by providing a full description of underlying disagreement among loci and sites. An easy to use implementation and tutorial is freely available in the IQ-TREE software package (http://www.iqtree.org/doc/Concordance-Factor, last accessed May 13, 2020).

ModelTest-NG: A New and Scalable Tool for the Selection of DNA and Protein Evolutionary Models.

ModelTest-NG is a reimplementation from scratch of jModelTest and ProtTest, two popular tools for selecting the best-fit nucleotide and amino acid substitution models, respectively. ModelTest-NG is one to two orders of magnitude faster than jModelTest and ProtTest but equally accurate and introduces several new features, such as ascertainment bias correction, mixture, and free-rate models, or the automatic processing of single partitions. ModelTest-NG is available under a GNU GPL3 license at https://github.com/ddarriba/modeltest , last accessed September 2, 2019.

Distinguishing Felsenstein Zone from Farris Zone Using Neural Networks.

Maximum likelihood and maximum parsimony are two key methods for phylogenetic tree reconstruction. Under certain conditions, each of these two methods can perform more or less efficiently, resulting in unresolved or disputed phylogenies. We show that a neural network can distinguish between four-taxon alignments that were evolved under conditions susceptible to either long-branch attraction or long-branch repulsion. When likelihood and parsimony methods are discordant, the neural network can provide insight as to which tree reconstruction method is best suited to the alignment. When applied to the contentious case of Strepsiptera evolution, our method shows robust support for the current scientific view, that is, it places Strepsiptera with beetles, distant from flies.

Deep Residual Neural Networks Resolve Quartet Molecular Phylogenies.

Phylogenetic inference is of fundamental importance to evolutionary as well as other fields of biology, and molecular sequences have emerged as the primary data for this task. Although many phylogenetic methods have been developed to explicitly take into account substitution models of sequence evolution, such methods could fail due to model misspecification or insufficiency, especially in the face of heterogeneities in substitution processes across sites and among lineages. In this study, we propose to infer topologies of four-taxon trees using deep residual neural networks, a machine learning approach needing no explicit modeling of the subject system and having a record of success in solving complex nonlinear inference problems. We train residual networks on simulated protein sequence data with extensive amino acid substitution heterogeneities. We show that the well-trained residual network predictors can outperform existing state-of-the-art inference methods such as the maximum likelihood method on diverse simulated test data, especially under extensive substitution heterogeneities. Reassuringly, residual network predictors generally agree with existing methods in the trees inferred from real phylogenetic data with known or widely believed topologies. Furthermore, when combined with the quartet puzzling algorithm, residual network predictors can be used to reconstruct trees with more than four taxa. We conclude that deep learning represents a powerful new approach to phylogenetic reconstruction, especially when sequences evolve via heterogeneous substitution processes. We present our best trained predictor in a freely available program named Phylogenetics by Deep Learning (PhyDL, https://gitlab.com/ztzou/phydl; last accessed January 3, 2020).

Sucrose-phosphate phosphatase from sugarcane reveals an ancestral tandem duplication.

BACKGROUND: Sugarcane is capable to store large amounts of sucrose in the culm at maturity hence it became a major source of sucrose for the food and the renewable energy industries. Sucrose, the main disaccharide produced by photosynthesis, is mainly stored in the vacuole of the cells of non-photosynthetic tissues. Two pathways are known to release free sucrose in plant cells, one is de novo synthesis dependent on sucrose phosphate synthase (SPS) and sucrose phosphate phosphatase (S6PP) while the other is regulatory and dependent on sucrose synthase (SuSy) activity. The molecular understanding of genes that give rise to the expression of the enzyme sucrose phosphate phosphatase, responsible for the release of sucrose in the last synthetic step lag behind the regulatory SuSy gene. RESULTS: Sugarcane genome sequencing effort disclosed the existence of a tandem duplication and the present work further support that both S6PP.1 and S6PP_2D isoforms are actively transcribed in young sugarcane plants but significantly less at maturity. Two commercial hybrids (SP80-3280 and R570) and both Saccharum spontaneum (IN84-58) and S.officinarum (BADILLA) exhibit transcriptional activity at three-month-old plants of the tandem S6PP_2D in leaves, culm, meristem and root system with a cultivar-specific distribution. Moreover, this tandem duplication is shared with other grasses and is ancestral in the group. CONCLUSION: Detection of a new isoform of S6PP resulting from the translation of 14 exon-containing transcript (S6PP_2D) will contribute to the knowledge of sucrose metabolism in plants. In addition, expression varies along plant development and between sugarcane cultivars and parental species.