scholarly journals Reversible Polymorphism-Aware Phylogenetic Models and their Application to Tree Inference

2016 ◽  
Author(s):  
Dominik Schrempf ◽  
Bui Quang Minh ◽  
Nicola De Maio ◽  
Arndt von Haeseler ◽  
Carolin Kosiol
Keyword(s):  
Large Scale ◽  
Incomplete Lineage Sorting ◽  
Species Tree ◽  
Species Trees ◽  
Lineage Sorting ◽  
Substitution Models ◽  
Tree Inference ◽  
Genome Wide Data ◽  
Tree Estimation ◽  
Dna Substitution

AbstractWe present a reversible Polymorphism-Aware Phylogenetic Model (revPoMo) for species tree estimation from genome-wide data. revPoMo enables the reconstruction of large scale species trees for many within-species samples. It expands the alphabet of DNA substitution models to include polymorphic states, thereby, naturally accounting for incomplete lineage sorting. We implemented revPoMo in the maximum likelihood software IQ-TREE. A simulation study and an application to great apes data show that the runtimes of our approach and standard substitution models are comparable but that revPoMo has much better accuracy in estimating trees, divergence times and mutation rates. The advantage of revPoMo is that an increase of sample size per species improves estimations but does not increase runtime. Therefore, revPoMo is a valuable tool with several applications, from speciation dating to species tree reconstruction.

scholarly journals Impact of Ghost Introgression on Coalescent-based Species Tree Inference and Estimation of Divergence Time

2022 ◽  
Author(s):  
XiaoXu Pang ◽  
Da-Yong Zhang
Keyword(s):  
Incomplete Lineage Sorting ◽  
Divergence Time ◽  
Species Tree ◽  
Gene Trees ◽  
Species Trees ◽  
Lineage Sorting ◽  
Multispecies Coalescent ◽  
Tree Inference ◽  
Tree Methods ◽  
The Impact

The species studied in any evolutionary investigation generally constitute a very small proportion of all the species currently existing or that have gone extinct. It is therefore likely that introgression, which is widespread across the tree of life, involves "ghosts," i.e., unsampled, unknown, or extinct lineages. However, the impact of ghost introgression on estimations of species trees has been rarely studied and is thus poorly understood. In this study, we use mathematical analysis and simulations to examine the robustness of species tree methods based on a multispecies coalescent model under gene flow sourcing from an extant or ghost lineage. We found that very low levels of extant or ghost introgression can result in anomalous gene trees (AGTs) on three-taxon rooted trees if accompanied by strong incomplete lineage sorting (ILS). In contrast, even massive introgression, with more than half of the recipient genome descending from the donor lineage, may not necessarily lead to AGTs. In cases involving an ingroup lineage (defined as one that diverged no earlier than the most basal species under investigation) acting as the donor of introgression, the time of root divergence among the investigated species was either underestimated or remained unaffected, but for the cases of outgroup ghost lineages acting as donors, the divergence time was generally overestimated. Under many conditions of ingroup introgression, the stronger the ILS was, the higher was the accuracy of estimating the time of root divergence, although the topology of the species tree is more prone to be biased by the effect of introgression.

scholarly journals MSCquartets 1.0: Quartet methods for species trees and networks under the multispecies coalescent model in R

2020 ◽  
Author(s):  
John A Rhodes ◽  
Hector Baños ◽  
Jonathan D Mitchell ◽  
Elizabeth S Allman
Keyword(s):  
Network Inference ◽  
Incomplete Lineage Sorting ◽  
R Package ◽  
Species Tree ◽  
Supplementary Information ◽  
Species Trees ◽  
Lineage Sorting ◽  
Coalescent Model ◽  
Multispecies Coalescent ◽  
Tree Inference

Abstract Summary MSCquartets is an R package for species tree hypothesis testing, inference of species trees, and inference of species networks under the Multispecies Coalescent model of incomplete lineage sorting and its network analog. Input for these analyses are collections of metric or topological locus trees which are then summarized by the quartets displayed on them. Results of hypothesis tests at user-supplied levels are displayed in a simplex plot by color-coded points. The package implements the QDC and WQDC algorithms for topological and metric species tree inference, and the NANUQ algorithm for level-1 topological species network inference, all of which give statistically consistent estimators under the model. Availability MSCquartets is available through the Comprehensive R Archive Network: https://CRAN.R-project.org/package=MSCquartets. Supplementary information Supplementary materials, including example data and analyses, are incorporated into the package.

scholarly journals Phylogenomic terraces: presence and implication in species tree estimation from gene trees

2020 ◽  
Author(s):  
Ishrat Tanzila Farah ◽  
Md Muktadirul Islam ◽  
Kazi Tasnim Zinat ◽  
Atif Hasan Rahman ◽  
Md Shamsuzzoha Bayzid
Keyword(s):  
Incomplete Lineage Sorting ◽  
Gene Tree ◽  
Species Tree ◽  
Gene Trees ◽  
Species Trees ◽  
Lineage Sorting ◽  
Deep Coalescence ◽  
Tree Estimation ◽  
Tree Space ◽  
Multiple Species

AbstractSpecies tree estimation from multi-locus dataset is extremely challenging, especially in the presence of gene tree heterogeneity across the genome due to incomplete lineage sorting (ILS). Summary methods have been developed which estimate gene trees and then combine the gene trees to estimate a species tree by optimizing various optimization scores. In this study, we have formalized the concept of “phylogenomic terraces” in the species tree space, where multiple species trees with distinct topologies may have exactly the same optimization score (quartet score, extra lineage score, etc.) with respect to a collection of gene trees. We investigated the presence and implication of terraces in species tree estimation from multi-locus data by taking ILS into account. We analyzed two of the most popular ILS-aware optimization criteria: maximize quartet consistency (MQC) and minimize deep coalescence (MDC). Methods based on MQC are provably statistically consistent, whereas MDC is not a consistent criterion for species tree estimation. Our experiments, on a collection of dataset simulated under ILS, indicate that MDC-based methods may achieve competitive or identical quartet consistency score as MQC but could be significantly worse than MQC in terms of tree accuracy – demonstrating the presence and affect of phylogenomic terraces. This is the first known study that formalizes the concept of phylogenomic terraces in the context of species tree estimation from multi-locus data, and reports the presence and implications of terraces in species tree estimation under ILS.

scholarly journals To include or not to include: The impact of gene filtering on species tree estimation methods

2017 ◽  
Author(s):  
Erin K. Molloy ◽  
Tandy Warnow
Keyword(s):  
Missing Data ◽  
Incomplete Lineage Sorting ◽  
Estimation Error ◽  
Gene Tree ◽  
Species Tree ◽  
Species Trees ◽  
Lineage Sorting ◽  
Gene Filtering ◽  
Tree Estimation ◽  
The Impact

AbstractSpecies tree estimation from loci sampled from multiple genomes is now common, but is challenged by the heterogeneity across the genome due to multiple processes, such as gene duplication and loss, horizontal gene transfer, and incomplete lineage sorting. Although methods for estimating species trees have been developed that address gene tree heterogeneity due to incomplete lineage sorting, many of these methods operate by combining estimated gene trees and are hence vulnerable to gene tree quality. There is also the added concern that missing data, which is frequently encountered in genome-scale datasets, will impact species tree estimation.Our study addresses the impact of gene filtering on species trees inferred from multi-gene datasets. We address these questions using a large and heterogeneous collection of simulated datasets both with and without missing data. We compare several established coalescent-based methods (ASTRAL, ASTRID, MP-EST, and SVDquartets within PAUP*) as well as unpartitioned concatenation using maximum likelihood (RAxML).Our study shows that gene tree error and missing data impact all methods (and some methods degrade more than others), but the degree of incomplete lineage sorting and gene tree estimation error impacts the absolute and relative performance of methods as well as their response to gene filtering strategies. We find that filtering genes based on the degree of missing data is either neutral or else reduces the accuracy of all five methods examined, and so is not recommended. Filtering genes based on gene tree estimation error shows somewhat different trends. Under low levels of incomplete lineage sorting, removing genes with high gene tree estimation error can improve the accuracy of summary methods, but only if not too many genes are removed. Otherwise, filtering genes tends to increase error, especially under high levels of incomplete lineage sorting. Hence, while filtering genes based on missing data is not recommended, there are conditions under which removing high error gene trees can improve species tree estimation. This study provides insights into prior studies and suggests approaches for analyzing phylogenomic datasets.

scholarly journals wQFM: Statistically Consistent Genome-scale Species Tree Estimation from Weighted Quartets

2020 ◽  
Author(s):  
Mahim Mahbub ◽  
Zahin Wahab ◽  
Rezwana Reaz ◽  
M. Saifur Rahman ◽  
Md. Shamsuzzoha Bayzid
Keyword(s):  
Incomplete Lineage Sorting ◽  
Gene Tree ◽  
Accurate Method ◽  
Species Tree ◽  
Estimation Methods ◽  
Gene Trees ◽  
Species Trees ◽  
Lineage Sorting ◽  
Tree Estimation ◽  
Source Form

AbstractMotivationSpecies tree estimation from genes sampled from throughout the whole genome is complicated due to the gene tree-species tree discordance. Incomplete lineage sorting (ILS) is one of the most frequent causes for this discordance, where alleles can coexist in populations for periods that may span several speciation events. Quartet-based summary methods for estimating species trees from a collection of gene trees are becoming popular due to their high accuracy and statistical guarantee under ILS. Generating quartets with appropriate weights, where weights correspond to the relative importance of quartets, and subsequently amalgamating the weighted quartets to infer a single coherent species tree allows for a statistically consistent way of estimating species trees. However, handling weighted quartets is challenging.ResultsWe propose wQFM, a highly accurate method for species tree estimation from multi-locus data, by extending the quartet FM (QFM) algorithm to a weighted setting. wQFM was assessed on a collection of simulated and real biological datasets, including the avian phylogenomic dataset which is one of the largest phylogenomic datasets to date. We compared wQFM with wQMC, which is the best alternate method for weighted quartet amalgamation, and with ASTRAL, which is one of the most accurate and widely used coalescent-based species tree estimation methods. Our results suggest that wQFM matches or improves upon the accuracy of wQMC and ASTRAL.AvailabilitywQFM is available in open source form at https://github.com/Mahim1997/wQFM-2020.

scholarly journals ASTRID: Accurate Species TRees from Internode Distances

2015 ◽  
Author(s):  
Pranjal Vachaspati ◽  
Tandy Warnow
Keyword(s):  
Good Accuracy ◽  
Incomplete Lineage Sorting ◽  
Current Method ◽  
Species Tree ◽  
Estimation Methods ◽  
Gene Trees ◽  
Species Trees ◽  
Lineage Sorting ◽  
Tree Estimation ◽  
Source Form

Background: Incomplete lineage sorting (ILS), modelled by the multi-species coalescent (MSC), is known to create discordance between gene trees and species trees, and lead to inaccurate species tree estimations unless appropriate methods are used to estimate the species tree. While many statistically consistent methods have been developed to estimate the species tree in the presence of ILS, only ASTRAL-2 and NJst have been shown to have good accuracy on large datasets. Yet, NJst is generally slower and less accurate than ASTRAL-2, and cannot run on some datasets. Results: We have redesigned NJst to enable it to run on all datasets, and we have expanded its design space so that it can be used with different distance-based tree estimation methods. The resultant method, ASTRID, is statistically consistent under the MSC model, and has accuracy that is competitive with ASTRAL-2. Furthermore, ASTRID is much faster than ASTRAL-2, completing in minutes on some datasets for which ASTRAL-2 used hours. Conclusions: ASTRID is a new coalescent-based method for species tree estimation that is competitive with the best current method in terms of accuracy, while being much faster. ASTRID is available in open source form on github.

scholarly journals ILS-Aware Analyses of Retroelement Insertions in the Anomaly Zone

2020 ◽  
Author(s):  
Erin K. Molloy ◽  
John Gatesy ◽  
Mark S. Springer
Keyword(s):  
Dna Sequences ◽  
Incomplete Lineage Sorting ◽  
Species Tree ◽  
Species Trees ◽  
Lineage Sorting ◽  
Short Branch ◽  
Multispecies Coalescent ◽  
Branch Lengths ◽  
Tree Estimation ◽  
Infinite Sites Model

AbstractA major shortcoming of concatenation methods for species tree estimation is their failure to account for incomplete lineage sorting (ILS). Coalescence methods explicitly 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 coalescence methods, retroelement insertions have emerged as powerful phylogenomic markers for species tree estimation. We show that two recently proposed methods, SDPquartets and ASTRAL_BP, are statistically consistent estimators of the species tree under the multispecies coalescent model, with retroelement insertions following a neutral infinite sites model of mutation. The accuracy of these and other methods for inferring species trees with retroelements has not been assessed in simulation studies. We simulate retroelements for four different species trees, including three with short branch lengths in the anomaly zone, and assess the performance of eight different methods for recovering the correct species tree. We also examine whether ASTRAL_BP recovers accurate internal branch lengths for internodes of various lengths (in coalescent units). Our results indicate that two recently proposed ILS-aware methods, ASTRAL_BP and SDPquartets, as well as the newly proposed ASTRID_BP, always recover the correct species tree on data sets with large numbers of retroelements even when there are extremely short species-tree branches in the anomaly zone. Dollo parsimony performed almost as well as these ILS-aware methods. By contrast, unordered parsimony, polymorphism parsimony, and MDC recovered the correct species tree in the case of a pectinate tree with four ingroup taxa in the anomaly zone, but failed to recover the correct tree in more complex anomaly-zone situations with additional lineages impacted by extensive incomplete lineage sorting. Camin-Sokal parsimony always reconstructed an incorrect tree in the anomaly zone. ASTRAL_BP accurately estimated branch lengths when internal branches were very short as in anomaly zone situations, but branch lengths were upwardly biased by more than 35% when species tree branches were longer. We derive a mathematical correction for these distortions, assuming the expected number of new retroelement insertions per generation is constant across the species tree. We also show that short branches do not need to be corrected even when this assumption does not hold; therefore, the branch lengths estimates produced by ASTRAL_BP may provide insight into whether an estimated species tree is in the anomaly zone.

scholarly journals STELAR: A statistically consistent coalescent-based species tree estimation method by maximizing triplet consistency

2019 ◽  
Author(s):  
Mazharul Islam ◽  
Kowshika Sarker ◽  
Trisha Das ◽  
Rezwana Reaz ◽  
Md. Shamsuzzoha Bayzid
Keyword(s):  
Incomplete Lineage Sorting ◽  
Gene Tree ◽  
Estimation Method ◽  
Species Tree ◽  
Mcmc Methods ◽  
Consistent Estimate ◽  
Gene Trees ◽  
Species Trees ◽  
Lineage Sorting ◽  
Tree Estimation

AbstractBackgroundSpecies tree estimation is frequently based on phylogenomic approaches that use multiple genes from throughout the genome. However, estimating a species tree from a collection of gene trees can be complicated due to the presence of gene tree incongruence resulting from incomplete lineage sorting (ILS), which is modelled by the multi-species coalescent process. Maximum likelihood and Bayesian MCMC methods can potentially result in accurate trees, but they do not scale well to large datasets.ResultsWe present STELAR (Species Tree Estimation by maximizing tripLet AgReement), a new fast and highly accurate statistically consistent coalescent-based method for estimating species trees from a collection of gene trees. We formalized the constrained triplet consensus (CTC) problem and showed that the solution to the CTC problem is a statistically consistent estimate of the species tree under the multi-species coalescent (MSC) model. STELAR is an efficient dynamic programming based solution to the CTC problem which is highly accurate and scalable. We evaluated the accuracy of STELAR in comparison with SuperTriplets, which is an alternate fast and highly accurate triplet-based supertree method, and with MP-EST and ASTRAL – two of the most popular and accurate coalescent-based methods. Experimental results suggest that STELAR matches the accuracy of ASTRAL and improves on MP-EST and SuperTriplets.ConclusionsTheoretical and empirical results (on both simulated and real biological datasets) suggest that STELAR is a valuable technique for species tree estimation from gene tree distributions.

scholarly journals PoMo: An Allele Frequency-based Approach for Species Tree Estimation

2015 ◽  
Author(s):  
Nicola De Maio ◽  
Dominik Schrempf ◽  
Carolin Kosiol
Keyword(s):  
Allele Frequency ◽  
Phylogenetic Trees ◽  
Incomplete Lineage Sorting ◽  
Species Tree ◽  
Efficient Estimation ◽  
Species Variation ◽  
Species Trees ◽  
Lineage Sorting ◽  
Genome Wide ◽  
Tree Estimation

Incomplete lineage sorting can cause incongruencies of the overall species-level phylogenetic tree with the phylogenetic trees for individual genes or genomic segments. If these incongruencies are not accounted for, it is possible to incur several biases in species tree estimation. Here, we present a simple maximum likelihood approach that accounts for ancestral variation and incomplete lineage sorting. We use a POlymorphisms-aware phylogenetic MOdel (PoMo) that we have recently shown to efficiently estimate mutation rates and fixation biases from within and between-species variation data. We extend this model to perform efficient estimation of species trees. We test the performance of PoMo in several different scenarios of incomplete lineage sorting using simulations and compare it with existing methods both in accuracy and computational speed. In contrast to other approaches, our model does not use coalescent theory but is allele-frequency based. We show that PoMo is well suited for genome-wide species tree estimation and that on such data it is more accurate than previous approaches.

scholarly journals MSCquartets 1.0: Quartet methods for species trees and networks under the multispecies coalescent model in R

2020 ◽  
Author(s):  
John A. Rhodes ◽  
Hector Baños ◽  
Jonathan D. Mitchell ◽  
Elizabeth S. Allman
Keyword(s):  
Network Inference ◽  
Incomplete Lineage Sorting ◽  
R Package ◽  
Species Tree ◽  
Species Trees ◽  
Lineage Sorting ◽  
Coalescent Model ◽  
Multispecies Coalescent ◽  
Tree Inference ◽  
Species Tree Inference

AbstractMSCquartets is an R package for species tree hypothesis testing, inference of species trees, and inference of species networks under the Multispecies Coalescent model of incomplete lineage sorting. Input for these analyses are collections of metric or topological locus trees which are then summarized by the quartets displayed on them. Results of hypothesis tests at user-supplied levels are displayed in a simplex plot by color-coded points. The package includes the QDC and WQDC algorithms for topological and metric species tree inference, and the NANUQ algorithm for level-1 topological species network inference, all of which give statistically consistent estimators under the model.

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