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

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

10.1101/2019.12.12.874727 ◽

2019 ◽

Cited By ~ 4

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.

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

10.1101/2021.03.29.437460 ◽

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.

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Phylogenetic conflicts, combinability, and deep phylogenomics in plants

10.1101/371930 ◽

2018 ◽

Cited By ~ 1

Author(s):

Stephen A. Smith ◽

Nathanael Walker-Hale ◽

Joseph F. Walker ◽

Joseph W. Brown

Keyword(s):

Phylogenetic Relationships ◽

Phylogenetic Signal ◽

Gene Tree ◽

Species Tree ◽

Gene Trees ◽

Data Filtering ◽

Tree Inference ◽

Tree Methods ◽

Inference Methods ◽

Species Tree Inference

AbstractStudies have demonstrated that pervasive gene tree conflict underlies several important phylogenetic relationships where different species tree methods produce conflicting results. Here, we present a means of dissecting the phylogenetic signal for alternative resolutions within a dataset in order to resolve recalcitrant relationships and, importantly, identify what the dataset is unable to resolve. These procedures extend upon methods for isolating conflict and concordance involving specific candidate relationships and can be used to identify systematic error and disambiguate sources of conflict among species tree inference methods. We demonstrate these on a large phylogenomic plant dataset. Our results support the placement of Amborella as sister to the remaining extant angiosperms, Gnetales as sister to pines, and the monophyly of extant gymnosperms. Several other contentious relationships, including the resolution of relationships within the bryophytes and the eudicots, remain uncertain given the low number of supporting gene trees. To address whether concatenation of filtered genes amplified phylogenetic signal for relationships, we implemented a combinatorial heuristic to test combinability of genes. We found that nested conflicts limited the ability of data filtering methods to fully ameliorate conflicting signal amongst gene trees. These analyses confirmed that the underlying conflicting signal does not support broad concatenation of genes. Our approach provides a means of dissecting a specific dataset to address deep phylogenetic relationships while also identifying the inferential boundaries of the dataset.

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Practical Speedup of Bayesian Inference of Species Phylogenies by Restricting the Space of Gene Trees

10.1101/770784 ◽

2019 ◽

Author(s):

Yaxuan Wang ◽

Huw A. Ogilvie ◽

Luay Nakhleh

Keyword(s):

Bayesian Inference ◽

Species Tree ◽

Biological Data ◽

Small Data ◽

Data Sets ◽

Gene Trees ◽

Multispecies Coalescent ◽

Tree Inference ◽

The Individual ◽

Species Tree Inference

AbstractSpecies tree inference from multi-locus data has emerged as a powerful paradigm in the post-genomic era, both in terms of the accuracy of the species tree it produces as well as in terms of elucidating the processes that shaped the evolutionary history. Bayesian methods for species tree inference are desirable in this area as they have been shown to yield accurate estimates, but also to naturally provide measures of confidence in those estimates. However, the heavy computational requirements of Bayesian inference have limited the applicability of such methods to very small data sets.In this paper, we show that the computational efficiency of Bayesian inference under the multispecies coalescent can be improved in practice by restricting the space of the gene trees explored during the random walk, without sacrificing accuracy as measured by various metrics. The idea is to first infer constraints on the trees of the individual loci in the form of unresolved gene trees, and then to restrict the sampler to consider only resolutions of the constrained trees. We demonstrate the improvements gained by such an approach on both simulated and biological data.

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Practical Speedup of Bayesian Inference of Species Phylogenies by Restricting the Space of Gene Trees

Molecular Biology and Evolution ◽

10.1093/molbev/msaa045 ◽

2020 ◽

Vol 37 (6) ◽

pp. 1809-1818

Author(s):

Yaxuan Wang ◽

Huw A Ogilvie ◽

Luay Nakhleh

Keyword(s):

Bayesian Inference ◽

Species Tree ◽

Biological Data ◽

Small Data ◽

Data Sets ◽

Gene Trees ◽

Multispecies Coalescent ◽

Tree Inference ◽

The Individual ◽

Species Tree Inference

Abstract Species tree inference from multilocus data has emerged as a powerful paradigm in the postgenomic era, both in terms of the accuracy of the species tree it produces as well as in terms of elucidating the processes that shaped the evolutionary history. Bayesian methods for species tree inference are desirable in this area as they have been shown not only to yield accurate estimates, but also to naturally provide measures of confidence in those estimates. However, the heavy computational requirements of Bayesian inference have limited the applicability of such methods to very small data sets. In this article, we show that the computational efficiency of Bayesian inference under the multispecies coalescent can be improved in practice by restricting the space of the gene trees explored during the random walk, without sacrificing accuracy as measured by various metrics. The idea is to first infer constraints on the trees of the individual loci in the form of unresolved gene trees, and then to restrict the sampler to consider only resolutions of the constrained trees. We demonstrate the improvements gained by such an approach on both simulated and biological data.

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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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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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Accounting for Uncertainty in Gene Tree Estimation: Summary-Coalescent Species Tree Inference in a Challenging Radiation of Australian Lizards

10.1101/056085 ◽

2016 ◽

Author(s):

Mozes P.K. Blom ◽

Jason G. Bragg ◽

Sally Potter ◽

Craig Moritz

Keyword(s):

Phylogenetic Signal ◽

Gene Tree ◽

Phylogenetic Inference ◽

Species Tree ◽

Gene Trees ◽

Tree Inference ◽

Tree Estimation ◽

Tree Resolution ◽

The Impact ◽

Species Tree Inference

AbstractAccurate gene tree inference is an important aspect of species tree estimation in a summary-coalescent framework. Yet, in empirical studies, inferred gene trees differ in accuracy due to stochastic variation in phylogenetic signal between targeted loci. Empiricists should therefore examine the consistency of species tree inference, while accounting for the observed heterogeneity in gene tree resolution of phylogenomic datasets. Here, we assess the impact of gene tree estimation error on summary-coalescent species tree inference by screening ~2000 exonic loci based on gene tree resolution prior to phylogenetic inference. We focus on a phylogenetically challenging radiation of Australian lizards (genus Cryptoblepharus, Scincidae) and explore effects on topology and support. We identify a well-supported topology based on all loci and find that a relatively small number of high-resolution gene trees can be sufficient to converge on the same topology. Adding gene trees with decreasing resolution produced a generally consistent topology, and increased confidence for specific bipartitions that were poorly supported when using a small number of informative loci. This corroborates coalescent-based simulation studies that have highlighted the need for a large number of loci to confidently resolve challenging relationships and refutes the notion that low-resolution gene trees introduce phylogenetic noise. Further, our study also highlights the value of quantifying changes in nodal support across locus subsets of increasing size (but decreasing gene tree resolution). Such detailed analyses can reveal anomalous fluctuations in support at some nodes, suggesting the possibility of model violation. By characterizing the heterogeneity in phylogenetic signal among loci, we can account for uncertainty in gene tree estimation and assess its effect on the consistency of the species tree estimate. We suggest that the evaluation of gene tree resolution should be incorporated in the analysis of empirical phylogenomic datasets. This will ultimately increase our confidence in species tree estimation using summary-coalescent methods and enable us to exploit genomic data for phylogenetic inference.

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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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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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