scholarly journals Scalable Species Tree Inference with External Constraints

2021 ◽  
Author(s):  
Baqiao Liu ◽  
Tandy Warnow
Keyword(s):  
Species Tree ◽  
Species Trees ◽  
Basic Step ◽  
Tree Inference ◽  
Speed Up ◽  
Biological Discovery ◽  
Source Form ◽  
Model Subject ◽  
Species Tree Inference ◽  
Constraint Tree

Species tree inference under the multi-species coalescent (MSC) model is a basic step in biological discovery. Despite the developments in recent years of methods that are proven statistically consistent and that have high accuracy, large datasets create computational challenges. Although there is gener- ally some information available about the species trees that could be used to speed up the estimation, only one method, ASTRAL-J, a recent development in the ASTRAL family of methods, is able to use this information. Here we describe two new methods, NJst-J and FASTRAL-J, that can estimate the species tree given partial knowledge of the species tree in the form of a non-binary unrooted constraint tree.. We show that both NJst-J and FASTRAL-J are much faster than ASTRAL-J and we prove that all three methods are statistically consistent under the multi-species coalescent model subject to this constraint. Our extensive simulation study shows that both FASTRAL-J and NJst-J provide advantages over ASTRAL-J: both are faster (and NJst-J is particularly fast), and FASTRAL-J is generally at least as accurate as ASTRAL-J. An analysis of the Avian Phylogenomics project dataset with 48 species and 14,446 genes presents additional evidence of the value of FASTRAL-J over ASTRAL-J (and both over ASTRAL), with dramatic reductions in running time (20 hours for default ASTRAL, and minutes or seconds for ASTRAL-J and FASTRAL-J, respectively). Availability: FASTRAL-J and NJst-J are available in open source form at https://github.com/ RuneBlaze/FASTRAL-constrained and https://github.com/RuneBlaze/NJst-constrained. Locations of the datasets used in this study and detailed commands needed to reproduce the study are provided in the supplementary materials at http://tandy.cs.illinois.edu/baqiao-suppl.pdf.

scholarly journals STAG: Species Tree Inference from All Genes

2018 ◽  
Author(s):  
D.M. Emms ◽  
S. Kelly
Keyword(s):  
Sequence Data ◽  
Phylogenetic Reconstruction ◽  
Gene Families ◽  
Single Copy ◽  
Species Tree ◽  
Species Trees ◽  
Molecular Sequence Data ◽  
Copy Gene ◽  
Tree Inference ◽  
Species Tree Inference

AbstractSpecies tree inference is fundamental to our understanding of the evolution of life on earth. However, species tree inference from molecular sequence data is complicated by gene duplication events that limit the availably of suitable data for phylogenetic reconstruction. Here we propose a novel method for species tree inference called STAG that is specifically designed to leverage data from multi-copy gene families. By application to 12 real species datasets sampled from across the eukaryotic domain we demonstrate that species trees inferred from multi-copy gene families are comparable in accuracy to species trees inferred from single-copy orthologues. We further show that the ability to utilise data from multi-copy gene families increases the amount of data available for species tree inference by an average of 8 fold. We reveal that on real species datasets STAG has higher accuracy than other leading methods for species tree inference; including concatenated alignments of protein sequences, ASTRAL & NJst. Finally we show that STAG is fast, memory efficient and scalable and thus suitable for analysis of large multispecies datasets.

scholarly journals Evolutionary patterns of 64 vertebrate genomes (species) revealed by phylogenomics analysis of protein-coding gene families

2020 ◽  
Author(s):  
Jia Song ◽  
Xia Han ◽  
Kui Lin
Keyword(s):  
Gene Family ◽  
Gene Families ◽  
Species Tree ◽  
Whole Genome ◽  
Species Trees ◽  
High Concentration ◽  
Evolutionary Mechanisms ◽  
Study Results ◽  
Tree Inference ◽  
Species Tree Inference

AbstractBackgroundRecent studies have demonstrated that phylogenomics is an important basis for answering many fundamental evolutionary questions. With more high-quality whole genome sequences published, more efficient phylogenomics analysis workflows are required urgently.ResultsTo this end and in order to capture putative differences among evolutionary histories of gene families and species, we developed a phylogenomics workflow for gene family classification, gene family tree inference, species tree inference and duplication/loss events dating. Our analysis framework is on the basis of two guiding ideas: 1) gene trees tend to be different from species trees but they influence each other in evolution; 2) different gene families have undergone different evolutionary mechanisms. It has been applied to the genomic data from 64 vertebrates and 5 out-group species. And the results showed high accuracy on species tree inference and few false-positives in duplication events dating.ConclusionsBased on the inferred gene duplication and loss event, only 9∼16% gene families have duplication retention after a whole genome duplication (WGD) event. A large part of these families have ohnologs from two or three WGDs. Consistent with the previous study results, the gene function of these families are mainly involved in nervous system and signal transduction related biological processes. Specifically, we found that the gene families with ohnologs from the teleost-specific (TS) WGD are enriched in fat metabolism, this result implyng that the retention of such ohnologs might be associated with the environmental status of high concentration of oxygen during that period.

scholarly journals ASTRAL-Pro: quartet-based species tree inference despite paralogy

2019 ◽  
Author(s):  
Chao Zhang ◽  
Celine Scornavacca ◽  
Erin K. Molloy ◽  
Siavash Mirarab
Keyword(s):  
Gene Duplication ◽  
Gene Loss ◽  
Incomplete Lineage Sorting ◽  
Single Copy ◽  
Species Tree ◽  
Alternative Methods ◽  
Gene Trees ◽  
Species Trees ◽  
Tree Inference ◽  
Species Tree Inference

AbstractSpecies tree inference via summary methods that combine gene trees has become an increasingly common analysis in recent phylogenomic studies. This broad adoption has been partly due to the greater availability of genome-wide data and ample recognition that gene trees and species trees can differ due to biological processes such as gene duplication and gene loss. This increase has also been encouraged by the recent development of accurate and scalable summary methods, such as ASTRAL. However, most of these methods, including ASTRAL, can only handle single-copy gene trees and do not attempt to model gene duplication and gene loss. In this paper, we introduce a measure of quartet similarity between single-copy and multi-copy trees (accounting for orthology and paralogy relationships) that can be optimized via a scalable dynamic programming similar to the one used by ASTRAL. We then present a new quartet-based species tree inference method: ASTRAL-Pro (ASTRAL for PaRalogs and Orthologs). By studying its performance on an extensive collection of simulated datasets and on a real plant dataset, we show that ASTRAL-Pro is more accurate than alternative methods when gene trees differ from the species tree due to the simultaneous presence of gene duplication, gene loss, incomplete lineage sorting, and estimation errors.

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 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 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 SpeciesRax: A tool for maximum likelihood species tree inference from gene family trees under duplication, transfer, and loss.

2021 ◽  
Author(s):  
Benoit Morel ◽  
Paul Schade ◽  
Sarah Lutteropp ◽  
Tom A. Williams ◽  
Gergely J. Szöllösi ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Gene Family ◽  
Gene Families ◽  
Species Tree ◽  
Likelihood Method ◽  
Gene Trees ◽  
Informative Signal ◽  
Tree Inference ◽  
Species Tree Inference ◽  
Family Trees

Species tree inference from gene family trees is becoming increasingly popular because it can account for discordance between the species tree and the corresponding gene family trees. In particular, methods that can account for multiple-copy gene families exhibit potential to leverage paralogy as informative signal. At present, there does not exist any widely adopted inference method for this purpose. Here, we present SpeciesRax, the first maximum likelihood method that can infer a rooted species tree from a set of gene family trees and can account for gene duplication, loss, and transfer events. By explicitly modelling events by which gene trees can depart from the species tree, SpeciesRax leverages the phylogenetic rooting signal in gene trees. SpeciesRax infers species tree branch lengths in units of expected substitutions per site and branch support values via paralogy-aware quartets extracted from the gene family trees. Using both empirical and simulated datasets we show that SpeciesRax is at least as accurate as the best competing methods while being one order of magnitude faster on large datasets at the same time. We used SpeciesRax to infer a biologically plausible rooted phylogeny of the vertebrates comprising $188$ species from $31612$ gene families in one hour using $40$ cores. SpeciesRax is available under GNU GPL at https://github.com/BenoitMorel/GeneRax and on BioConda.

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.

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