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.

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 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 Species Tree Inference under the Multispecies Coalescent on Data with Paralogs is Accurate

2018 ◽  
Author(s):  
Zhi Yan ◽  
Peng Du ◽  
Matthew W. Hahn ◽  
Luay Nakhleh
Keyword(s):  
Incomplete Lineage Sorting ◽  
Single Copy ◽  
Species Tree ◽  
Biological Data ◽  
Lineage Sorting ◽  
Multispecies Coalescent ◽  
Gene Copies ◽  
Tree Inference ◽  
Inference Methods ◽  
Species Tree Inference

AbstractThe multispecies coalescent (MSC) has emerged as a powerful and desirable framework for species tree inference in phylogenomic studies. Under this framework, the data for each locus is assumed to consist of orthologous, single-copy genes, and heterogeneity across loci is assumed to be due to incomplete lineage sorting (ILS). These assumptions have led biologists that use ILS-aware inference methods, whether based directly on the MSC or proven to be statistically consistent under it (collectively referred to here as MSC-based methods), to exclude all loci that are present in more than a single copy in any of the studied genomes. Furthermore, such analyses entail orthology assignment to avoid the potential of hidden paralogy in the data. The question we seek to answer in this study is: What happens if one runs such species tree inference methods on data where paralogy is present, in addition to or without ILS being present? Through simulation studies and analyses of two biological data sets, we show that running such methods on data with paralogs provide very accurate results, either by treating all gene copies within a family as alleles from multiple individuals or by randomly selecting one copy per species. Our results have significant implications for the use of MSC-based phylogenomic analyses, demonstrating that they do not have to be restricted to single-copy loci, thus greatly increasing the amount of data that can be used. [Multispecies coalescent; incomplete lineage sorting; gene duplication and loss; orthology; paralogy.]

scholarly journals Identifying model violations under the multispecies coalescent model using P2C2M.SNAPP

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8271
Author(s):  
Drew J. Duckett ◽  
Tara A. Pelletier ◽  
Bryan C. Carstens
Keyword(s):  
Posterior Distribution ◽  
Incomplete Lineage Sorting ◽  
R Package ◽  
Predictive Distribution ◽  
Species Trees ◽  
Lineage Sorting ◽  
Posterior Predictive Distribution ◽  
Coalescent Model ◽  
Multispecies Coalescent ◽  
Phylogenetic Estimation

Phylogenetic estimation under the multispecies coalescent model (MSCM) assumes all incongruence among loci is caused by incomplete lineage sorting. Therefore, applying the MSCM to datasets that contain incongruence that is caused by other processes, such as gene flow, can lead to biased phylogeny estimates. To identify possible bias when using the MSCM, we present P2C2M.SNAPP. P2C2M.SNAPP is an R package that identifies model violations using posterior predictive simulation. P2C2M.SNAPP uses the posterior distribution of species trees output by the software package SNAPP to simulate posterior predictive datasets under the MSCM, and then uses summary statistics to compare either the empirical data or the posterior distribution to the posterior predictive distribution to identify model violations. In simulation testing, P2C2M.SNAPP correctly classified up to 83% of datasets (depending on the summary statistic used) as to whether or not they violated the MSCM model. P2C2M.SNAPP represents a user-friendly way for researchers to perform posterior predictive model checks when using the popular SNAPP phylogenetic estimation program. It is freely available as an R package, along with additional program details and tutorials.

scholarly journals Analyzing contentious relationships and outlier genes in phylogenomics

2017 ◽  
Author(s):  
Joseph F. Walker ◽  
Joseph W. Brown ◽  
Stephen A. Smith
Keyword(s):  
Incomplete Lineage Sorting ◽  
Species Tree ◽  
Gene Trees ◽  
Species Trees ◽  
Lineage Sorting ◽  
Tree Inference ◽  
Small Set ◽  
Tree Methods ◽  
Edge Based ◽  
Species Tree Inference

ABSTRACTRecent studies have demonstrated that conflict is common among gene trees in phylogenomic studies, and that less than one percent of genes may ultimately drive species tree inference in supermatrix analyses. Here, we examined two datasets where supermatrix and coalescent-based species trees conflict. We identified two highly influential “outlier” genes in each dataset. When removed from each dataset, the inferred supermatrix trees matched the topologies obtained from coalescent analyses. We also demonstrate that, while the outlier genes in the vertebrate dataset have been shown in a previous study to be the result of errors in orthology detection, the outlier genes from a plant dataset did not exhibit any obvious systematic error and therefore may be the result of some biological process yet to be determined. While topological comparisons among a small set of alternate topologies can be helpful in discovering outlier genes, they can be limited in several ways, such as assuming all genes share the same topology. Coalescent species tree methods relax this assumption but do not explicitly facilitate the examination of specific edges. Coalescent methods often also assume that conflict is the result of incomplete lineage sorting (ILS). Here we explored a framework that allows for quickly examining alternative edges and support for large phylogenomic datasets that does not assume a single topology for all genes. For both datasets, these analyses provided detailed results confirming the support for coalescent-based topologies. This framework suggests that we can improve our understanding of the underlying signal in phylogenomic datasets by asking more targeted edge-based questions.

scholarly journals Disentangling Sources of Gene Tree Discordance in Phylogenomic Data Sets: Testing Ancient Hybridizations in Amaranthaceae s.l

2020 ◽  
Author(s):  
Diego F Morales-Briones ◽  
Gudrun Kadereit ◽  
Delphine T Tefarikis ◽  
Michael J Moore ◽  
Stephen A Smith ◽  
...  
Keyword(s):  
Network Inference ◽  
Incomplete Lineage Sorting ◽  
Gene Tree ◽  
Phylogenetic Network ◽  
Species Tree ◽  
Data Sets ◽  
Species Trees ◽  
Lineage Sorting ◽  
Data Set ◽  
Gene Tree Discordance

Abstract Gene tree discordance in large genomic data sets can be caused by evolutionary processes such as incomplete lineage sorting and hybridization, as well as model violation, and errors in data processing, orthology inference, and gene tree estimation. Species tree methods that identify and accommodate all sources of conflict are not available, but a combination of multiple approaches can help tease apart alternative sources of conflict. Here, using a phylotranscriptomic analysis in combination with reference genomes, we test a hypothesis of ancient hybridization events within the plant family Amaranthaceae s.l. that was previously supported by morphological, ecological, and Sanger-based molecular data. The data set included seven genomes and 88 transcriptomes, 17 generated for this study. We examined gene-tree discordance using coalescent-based species trees and network inference, gene tree discordance analyses, site pattern tests of introgression, topology tests, synteny analyses, and simulations. We found that a combination of processes might have generated the high levels of gene tree discordance in the backbone of Amaranthaceae s.l. Furthermore, we found evidence that three consecutive short internal branches produce anomalous trees contributing to the discordance. Overall, our results suggest that Amaranthaceae s.l. might be a product of an ancient and rapid lineage diversification, and remains, and probably will remain, unresolved. This work highlights the potential problems of identifiability associated with the sources of gene tree discordance including, in particular, phylogenetic network methods. Our results also demonstrate the importance of thoroughly testing for multiple sources of conflict in phylogenomic analyses, especially in the context of ancient, rapid radiations. We provide several recommendations for exploring conflicting signals in such situations. [Amaranthaceae; gene tree discordance; hybridization; incomplete lineage sorting; phylogenomics; species network; species tree; transcriptomics.]

scholarly journals Divergence estimation in the presence of incomplete lineage sorting and migration

2017 ◽  
Author(s):  
Graham Jones
Keyword(s):  
Incomplete Lineage Sorting ◽  
Simulated Data ◽  
Species Tree ◽  
Lineage Sorting ◽  
Data Set ◽  
Migration Rates ◽  
Isolation With Migration ◽  
Multispecies Coalescent ◽  
Tree Inference ◽  
And Migration

AbstractThis paper focuses on the problem of estimating a species tree from multilocus data in the presence of incomplete lineage sorting and migration. We develop a mathematical model similar to IMa2 (Hey 2010) for the relevant evolutionary processes which allows both the the population size parameters and the migration rates between pairs of species tree branches to be integrated out. We then describe a BEAST2 package DENIM which based on this model, and which uses an approximation to sample from the posterior. The approximation is based on the assumption that migrations are rare, and it only samples from certain regions of the posterior which seem likely given this assumption. The method breaks down if there is a lot of migration. Using simulations, Leaché et al 2014 showed migration causes problems for species tree inference using the multispecies coalescent when migration is present but ignored. We re-analyze this simulated data to explore DENIM’s performance, and demonstrate substantial improvements over *BEAST. We also re-analyze an empirical data set. [isolation-with-migration; incomplete lineage sorting; multispecies coalescent; species tree; phylogenetic analysis; Bayesian; Markov chain Monte Carlo]

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.

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