ASTRAL-Pro: Quartet-Based Species-Tree Inference despite Paralogy.
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ABSTRACT:
SUBMITTER: Zhang C
PROVIDER: S-EPMC7751180 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:MotivationSpecies trees provide insight into basic biology, including the mechanisms of evolution and how it modifies biomolecular function and structure, biodiversity and co-evolution between genes and species. Yet, gene trees often differ from species trees, creating challenges to species tree estimation. One of the most frequent causes for conflicting topologies between gene trees and species trees is incomplete lineage sorting (ILS), which is modelled by the multi-species coalescent. While many methods have been developed to estimate species trees from multiple genes, some which have statistical guarantees under the multi-species coalescent model, existing methods are too computationally intensive for use with genome-scale analyses or have been shown to have poor accuracy under some realistic conditions.ResultsWe present ASTRAL, a fast method for estimating species trees from multiple genes. ASTRAL is statistically consistent, can run on datasets with thousands of genes and has outstanding accuracy-improving on MP-EST and the population tree from BUCKy, two statistically consistent leading coalescent-based methods. ASTRAL is often more accurate than concatenation using maximum likelihood, except when ILS levels are low or there are too few gene trees.Availability and implementationASTRAL is available in open source form at https://github.com/smirarab/ASTRAL/. Datasets studied in this article are available at http://www.cs.utexas.edu/users/phylo/datasets/astral.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC4147915 | biostudies-literature
Project description:BackgroundTo account for genome-wide discordance among gene trees, several widely-used methods seek to find a species tree with the minimum distance to input gene trees. To efficiently explore the large space of species trees, some of these methods, including ASTRAL, use dynamic programming (DP). The DP paradigm can restrict the search space, and thus, ASTRAL and similar methods use heuristic methods to define a restricted search space. However, arbitrary constraints provided by the user on the output tree cannot be trivially incorporated into such restrictions. The ability to infer trees that honor user-defined constraints is needed for many phylogenetic analyses, but no solution currently exists for constraining the output of ASTRAL.ResultsWe introduce methods that enable the ASTRAL dynamic programming to infer constrained trees in an effective and scalable manner. To do so, we adopt a recently developed tree completion algorithm and extend it to allow multifurcating input and output trees. In simulation studies, we show that the approach for honoring constraints is both effective and fast. On real data, we show that constrained searches can help interrogate branches not recovered in the optimal ASTRAL tree to reveal support for alternative hypotheses.ConclusionsThe new algorithm is added ASTRAL to all user-provided constraints on the species tree.
| S-EPMC7160890 | biostudies-literature
Project description:BackgroundInferring species trees from gene trees using the coalescent-based summary methods has been the subject of much attention, yet new scalable and accurate methods are needed.ResultsWe introduce DISTIQUE, a new statistically consistent summary method for inferring species trees from gene trees under the coalescent model. We generalize our results to arbitrary phylogenetic inference problems; we show that two arbitrarily chosen leaves, called anchors, can be used to estimate relative distances between all other pairs of leaves by inferring relevant quartet trees. This results in a family of distance-based tree inference methods, with running times ranging between quadratic to quartic in the number of leaves.ConclusionsWe show in simulated studies that DISTIQUE has comparable accuracy to leading coalescent-based summary methods and reduced running times.
| S-EPMC5123309 | biostudies-literature
Project description:The estimation of species phylogenies requires multiple loci, since different loci can have different trees due to incomplete lineage sorting, modeled by the multi-species coalescent model. We recently developed a coalescent-based method, ASTRAL, which is statistically consistent under the multi-species coalescent model and which is more accurate than other coalescent-based methods on the datasets we examined. ASTRAL runs in polynomial time, by constraining the search space using a set of allowed 'bipartitions'. Despite the limitation to allowed bipartitions, ASTRAL is statistically consistent.We present a new version of ASTRAL, which we call ASTRAL-II. We show that ASTRAL-II has substantial advantages over ASTRAL: it is faster, can analyze much larger datasets (up to 1000 species and 1000 genes) and has substantially better accuracy under some conditions. ASTRAL's running time is [Formula: see text], and ASTRAL-II's running time is [Formula: see text], where n is the number of species, k is the number of loci and X is the set of allowed bipartitions for the search space.ASTRAL-II is available in open source at https://github.com/smirarab/ASTRAL and datasets used are available at http://www.cs.utexas.edu/~phylo/datasets/astral2/.smirarab@gmail.comSupplementary data are available at Bioinformatics online.
| S-EPMC4765870 | biostudies-other
Project description:BACKGROUND:Evolutionary histories can be discordant across the genome, and such discordances need to be considered in reconstructing the species phylogeny. ASTRAL is one of the leading methods for inferring species trees from gene trees while accounting for gene tree discordance. ASTRAL uses dynamic programming to search for the tree that shares the maximum number of quartet topologies with input gene trees, restricting itself to a predefined set of bipartitions. RESULTS:We introduce ASTRAL-III, which substantially improves the running time of ASTRAL-II and guarantees polynomial running time as a function of both the number of species (n) and the number of genes (k). ASTRAL-III limits the bipartition constraint set (X) to grow at most linearly with n and k. Moreover, it handles polytomies more efficiently than ASTRAL-II, exploits similarities between gene trees better, and uses several techniques to avoid searching parts of the search space that are mathematically guaranteed not to include the optimal tree. The asymptotic running time of ASTRAL-III in the presence of polytomies is [Formula: see text] where D=O(nk) is the sum of degrees of all unique nodes in input trees. The running time improvements enable us to test whether contracting low support branches in gene trees improves the accuracy by reducing noise. In extensive simulations, we show that removing branches with very low support (e.g., below 10%) improves accuracy while overly aggressive filtering is harmful. We observe on a biological avian phylogenomic dataset of 14K genes that contracting low support branches greatly improve results. CONCLUSIONS:ASTRAL-III is a faster version of the ASTRAL method for phylogenetic reconstruction and can scale up to 10,000 species. With ASTRAL-III, low support branches can be removed, resulting in improved accuracy.
| S-EPMC5998893 | biostudies-literature
Project description:BACKGROUND:Deciphering the history of life on Earth has long been regarded as one of the most central tasks in biology. In past years, widespread discordance between the evolutionary histories of different groups of orthologous genes of prokaryotes have been revealed, primarily due to horizontal gene transfers (HGTs). Nonetheless, evidence that support a strong tree-like signal of evolution have been uncovered, despite the presence of HGT events. Therefore, a challenging task is to distill this tree-like signal from the noise induced by all sources of non-tree-like events. RESULTS:In this work we tackle this question, using real and simulated data. We first tighten a recent related theoretical result in this field. In a simulation study, we infer individual quartet topologies, and then use the inferred quartets to reconstruct simulated species trees. We demonstrate that accurate tree reconstruction is feasible despite surprisingly high rates of HGT. In a real data study, we construct phylogenies of two sets of prokaryotes, and show that our tree reconstruction scheme is comparable with (and complementary better than) other commonly used methods. CONCLUSIONS:Using a blend of theoretical and empirical investigations, our study proves the feasibility of accurate quartet-based phylogenetic reconstruction, the vast impact of HGT events notwithstanding.
| S-EPMC6101080 | biostudies-other
Project description:Several popular methods for phylogenetic inference (or hierarchical clustering) are based on a matrix of pairwise distances between taxa (or any kind of objects): The objective is to construct a tree with branch lengths so that the distances between the leaves in that tree are as close as possible to the input distances. If we hold the structure (topology) of the tree fixed, in some relevant cases (e.g., ordinary least squares) the optimal values for the branch lengths can be expressed using simple combinatorial formulae. Here we define a general form for these formulae and show that they all have two desirable properties: First, the common tree reconstruction approaches (least squares, minimum evolution), when used in combination with these formulae, are guaranteed to infer the correct tree when given enough data (consistency); second, the branch lengths of all the simple (nearest neighbor interchange) rearrangements of a tree can be calculated, optimally, in quadratic time in the size of the tree, thus allowing the efficient application of hill climbing heuristics. The study presented here is a continuation of that by Mihaescu and Pachter on branch length estimation [Mihaescu R, Pachter L (2008) Proc Natl Acad Sci USA 105:13206-13211]. The focus here is on the inference of the tree itself and on providing a basis for novel algorithms to reconstruct trees from distances.
| S-EPMC3478627 | biostudies-literature
Project description:Species delimitation and species tree inference are difficult problems in cases of recent divergence, especially when different loci have different histories. This paper quantifies the difficulty of jointly finding the division of samples to species and estimating a species tree without constraining the possible assignments a priori. It introduces a parametric and a nonparametric method, including new heuristic search strategies, to do this delimitation and tree inference using individual gene trees as input. The new methods were evaluated using thousands of simulations and 4 empirical data sets. These analyses suggest that the new methods, especially the nonparametric one, may provide useful insights for systematists working at the species level with molecular data. However, they still often return incorrect results.
| S-EPMC5841455 | biostudies-literature
Project description:Understanding cell differentiation-the process of generation of distinct cell-types-plays a pivotal role in developmental and evolutionary biology. Transcriptomic information and epigenetic marks are useful to elucidate hierarchical developmental relationships among cell-types. Standard phylogenetic approaches such as maximum parsimony, maximum likelihood and neighbor joining have previously been applied to ChIP-Seq histone modification data to infer cell-type trees, showing how diverse types of cells are related. In this study, we demonstrate the applicability and suitability of quartet-based phylogenetic tree estimation techniques for constructing cell-type trees. We propose two quartet-based pipelines for constructing cell phylogeny. Our methods were assessed for their validity in inferring hierarchical differentiation processes of various cell-types in H3K4me3, H3K27me3, H3K36me3, and H3K27ac histone mark data. We also propose a robust metric for evaluating cell-type trees.
| S-EPMC6762093 | biostudies-literature
Project description:The classic multispecies coalescent (MSC) model provides the means for theoretical justification of incomplete lineage sorting-aware species tree inference methods. This has motivated an extensive body of work on phylogenetic methods that are statistically consistent under MSC. One such particularly popular method is ASTRAL, a quartet-based species tree inference method. Novel studies suggest that ASTRAL also performs well when given multi-locus gene trees in simulation studies. Further, Legried et al. recently demonstrated that ASTRAL is statistically consistent under the gene duplication and loss model (GDL). GDL is prevalent in evolutionary histories and is the first core process in the powerful duplication-loss-coalescence evolutionary model (DLCoal) by Rasmussen and Kellis. In this work, we prove that ASTRAL is statistically consistent under the general DLCoal model. Therefore, our result supports the empirical evidence from the simulation-based studies. More broadly, we prove that the quartet-based inference approach is statistically consistent under DLCoal. Supplementary data are available at Bioinformatics online.
| S-EPMC9113308 | biostudies-literature