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

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.

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ASTRAL-Pro: Quartet-Based Species-Tree Inference despite Paralogy

Molecular Biology and Evolution ◽

10.1093/molbev/msaa139 ◽

2020 ◽

Vol 37 (11) ◽

pp. 3292-3307

Author(s):

Chao Zhang ◽

Celine Scornavacca ◽

Erin K Molloy ◽

Siavash Mirarab

Keyword(s):

Simulated Data ◽

Real Data ◽

Single Copy ◽

Species Tree ◽

Alternative Methods ◽

Data Sets ◽

Gene Trees ◽

Species Trees ◽

Tree Inference ◽

Genome Wide Data

Abstract Phylogenetic inference from genome-wide data (phylogenomics) has revolutionized the study of evolution because it enables accounting for discordance among evolutionary histories across the genome. To this end, summary methods have been developed to allow accurate and scalable inference of species trees from gene trees. However, most of these methods, including the widely used ASTRAL, can only handle single-copy gene trees and do not attempt to model gene duplication and gene loss. As a result, most phylogenomic studies have focused on single-copy genes and have discarded large parts of the data. Here, we first propose a measure of quartet similarity between single-copy and multicopy trees that accounts for orthology and paralogy. We then introduce a method called ASTRAL-Pro (ASTRAL for PaRalogs and Orthologs) to find the species tree that optimizes our quartet similarity measure using dynamic programing. By studying its performance on an extensive collection of simulated data sets and on real data sets, we show that ASTRAL-Pro is more accurate than alternative methods.

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Analyzing contentious relationships and outlier genes in phylogenomics

10.1101/115774 ◽

2017 ◽

Cited By ~ 3

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.

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

10.1101/2022.01.11.475787 ◽

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.

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

10.1101/498378 ◽

2018 ◽

Cited By ~ 10

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.]

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Consensus properties and their large-scale applications for the gene duplication problem

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720016420051 ◽

2016 ◽

Vol 14 (03) ◽

pp. 1642005 ◽

Cited By ~ 4

Author(s):

Jucheol Moon ◽

Harris T. Lin ◽

Oliver Eulenstein

Keyword(s):

Gene Duplication ◽

Large Scale ◽

Species Tree ◽

Gene Duplications ◽

Gene Trees ◽

Scalable Algorithm ◽

All Solutions ◽

Tree Inference ◽

Minimum Number ◽

Species Tree Inference

Solving the gene duplication problem is a classical approach for species tree inference from gene trees that are confounded by gene duplications. This problem takes a collection of gene trees and seeks a species tree that implies the minimum number of gene duplications. Wilkinson et al. posed the conjecture that the gene duplication problem satisfies the desirable Pareto property for clusters. That is, for every instance of the problem, all clusters that are commonly present in the input gene trees of this instance, called strict consensus, will also be found in every solution to this instance. We prove that this conjecture does not generally hold. Despite this negative result we show that the gene duplication problem satisfies a weaker version of the Pareto property where the strict consensus is found in at least one solution (rather than all solutions). This weaker property contributes to our design of an efficient scalable algorithm for the gene duplication problem. We demonstrate the performance of our algorithm in analyzing large-scale empirical datasets. Finally, we utilize the algorithm to evaluate the accuracy of standard heuristics for the gene duplication problem using simulated datasets.

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From Gene Trees to Species Trees II: Species Tree Inference by Minimizing Deep Coalescence Events

IEEE/ACM Transactions on Computational Biology and Bioinformatics ◽

10.1109/tcbb.2011.83 ◽

2011 ◽

Vol 8 (6) ◽

pp. 1685-1691 ◽

Cited By ~ 32

Author(s):

Louxin Zhang

Keyword(s):

Species Tree ◽

Gene Trees ◽

Species Trees ◽

Deep Coalescence ◽

Tree Inference ◽

Species Tree Inference

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STAG: Species Tree Inference from All Genes

10.1101/267914 ◽

2018 ◽

Cited By ~ 24

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.

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Comparing Methods for Species Tree Estimation With Gene Duplication and Loss

10.1101/2021.02.05.429947 ◽

2021 ◽

Author(s):

James Willson ◽

Mrinmoy Saha Roddur ◽

Tandy Warnow

Keyword(s):

Gene Duplication ◽

Data Bank ◽

Species Tree ◽

Biological Research ◽

Gene Trees ◽

Species Trees ◽

Tree Inference ◽

Multiple Copies ◽

Gene Duplication And Loss ◽

Tree Estimation

AbstractSpecies tree inference from gene trees is an important part of biological research. One confounding factor in estimating species trees is gene duplication and loss which can lead to gene trees with multiple copies of the same gene. In recent years there have been several new methods developed to address this problem that have substantially improved on earlier methods; however, the best performing methods (ASTRAL-Pro, ASTRID-multi, and FastMulRFS) have not yet been directly compared. In this study, we compare ASTRAL-Pro, ASTRID-multi, and FastMulRFS under a wide variety of conditions. Our study shows that while all three have very good accuracy, nearly the same under many conditions, ASTRAL-Pro and ASTRID-multi are more reliably accurate than FastMuLRFS, and that ASTRID-multi is often faster than ASTRAL-Pro. The datasets generated for this study are freely available in the Illinois Data Bank at https://databank.illinois.edu/datasets/IDB-2418574

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DISCO: Species Tree Inference using Multicopy Gene Family Tree Decomposition

Systematic Biology ◽

10.1093/sysbio/syab070 ◽

2021 ◽

Cited By ~ 1

Author(s):

James Willson ◽

Mrinmoy Saha Roddur ◽

Baqiao Liu ◽

Paul Zaharias ◽

Tandy Warnow

Keyword(s):

Gene Duplication ◽

Gene Family ◽

Gene Tree ◽

Single Copy ◽

Species Tree ◽

Family Tree ◽

Tree Inference ◽

Gene Duplication And Loss ◽

Species Tree Inference ◽

Family Trees

Abstract Species tree inference from gene family trees is a significant problem in computational biology. However, gene tree heterogeneity, which can be caused by several factors including gene duplication and loss, makes the estimation of species trees very challenging. While there have been several species tree estimation methods introduced in recent years to specifically address gene tree heterogeneity due to gene duplication and loss (such as DupTree, FastMulRFS, ASTRAL-Pro, and SpeciesRax), many incur high cost in terms of both running time and memory. We introduce a new approach, DISCO, that decomposes the multi-copy gene family trees into many single copy trees, which allows for methods previously designed for species tree inference in a single copy gene tree context to be used. We prove that using DISCO with ASTRAL (i.e., ASTRAL-DISCO) is statistically consistent under the GDL model, provided that ASTRAL-Pro correctly roots and tags each gene family tree. We evaluate DISCO paired with different methods for estimating species trees from single copy genes (e.g., ASTRAL, ASTRID, and IQ-TREE) under a wide range of model conditions, and establish that high accuracy can be obtained even when ASTRAL-Pro is not able to correctly roots and tags the gene family trees. We also compare results using MI, an alternative decomposition strategy from Yang Y. and Smith S.A. (2014), and find that DISCO provides better accuracy, most likely as a result of covering more of the gene family tree leafset in the output decomposition. [Concatenation analysis; gene duplication and loss; species tree inference; summary method.]

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

10.1101/2020.05.01.073361 ◽

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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