scholarly journals Comparing Methods for Species Tree Estimation with Gene Duplication and Loss

2021 ◽  
pp. 106-117
Author(s):  
James Willson ◽  
Mrinmoy Saha Roddur ◽  
Tandy Warnow
Keyword(s):  
Gene Duplication ◽  
Species Tree ◽  
Gene Duplication And Loss ◽  
Tree Estimation

scholarly journals FastMulRFS: fast and accurate species tree estimation under generic gene duplication and loss models

2020 ◽  
Vol 36 (Supplement_1) ◽  
pp. i57-i65 ◽  
Author(s):  
Erin K Molloy ◽  
Tandy Warnow
Keyword(s):  
Gene Duplication ◽  
Species Tree ◽  
Supplementary Information ◽  
Biological Research ◽  
Generic Model ◽  
Species Trees ◽  
Basic Part ◽  
Gene Duplication And Loss ◽  
Tree Estimation ◽  
Scalable Methods

Abstract Motivation Species tree estimation is a basic part of biological research but can be challenging because of gene duplication and loss (GDL), which results in genes that can appear more than once in a given genome. All common approaches in phylogenomic studies either reduce available data or are error-prone, and thus, scalable methods that do not discard data and have high accuracy on large heterogeneous datasets are needed. Results We present FastMulRFS, a polynomial-time method for estimating species trees without knowledge of orthology. We prove that FastMulRFS is statistically consistent under a generic model of GDL when adversarial GDL does not occur. Our extensive simulation study shows that FastMulRFS matches the accuracy of MulRF (which tries to solve the same optimization problem) and has better accuracy than prior methods, including ASTRAL-multi (the only method to date that has been proven statistically consistent under GDL), while being much faster than both methods. Availability and impementation FastMulRFS is available on Github (https://github.com/ekmolloy/fastmulrfs). Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals FastMulRFS: Fast and accurate species tree estimation under generic gene duplication and loss models

2019 ◽  
Author(s):  
Erin K. Molloy ◽  
Tandy Warnow
Keyword(s):  
Gene Duplication ◽  
Species Tree ◽  
Biological Research ◽  
Generic Model ◽  
Species Trees ◽  
Basic Part ◽  
Heterogeneous Datasets ◽  
Gene Duplication And Loss ◽  
Tree Estimation ◽  
Scalable Methods

AbstractMotivationSpecies tree estimation is a basic part of biological research but can be challenging because of gene duplication and loss (GDL), which results in genes that can appear more than once in a given genome. All common approaches in phylogenomic studies either reduce available data or are error-prone, and thus, scalable methods that do not discard data and have high accuracy on large heterogeneous datasets are needed.ResultsWe present FastMulRFS, a polynomial-time method for estimating species trees without knowledge of orthology. We prove that FastMulRFS is statistically consistent under a generic model of GDL when adversarial GDL does not occur. Our extensive simulation study shows that FastMulRFS matches the accuracy of MulRF (which tries to solve the same optimization problem) and has better accuracy than prior methods, including ASTRAL-multi (the only method to date that has been proven statistically consistent under GDL), while being much faster than both methods.AvailabilityFastMulRFS is available on Github (https://github.com/ekmolloy/fastmulrfs).

scholarly journals Comparing Methods for Species Tree Estimation With Gene Duplication and Loss

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

scholarly journals Inference of Ancient Whole-Genome Duplications and the Evolution of Gene Duplication and Loss Rates

2019 ◽  
Vol 36 (7) ◽  
pp. 1384-1404 ◽  
Author(s):  
Arthur Zwaenepoel ◽  
Yves Van de Peer
Keyword(s):  
Maximum Likelihood ◽  
Gene Duplication ◽  
Gene Tree ◽  
Probabilistic Approach ◽  
Species Tree ◽  
Rate Variation ◽  
Whole Genome ◽  
Tree Reconciliation ◽  
Gene Duplication And Loss ◽  
Loss Rates

Abstract Gene tree–species tree reconciliation methods have been employed for studying ancient whole-genome duplication (WGD) events across the eukaryotic tree of life. Most approaches have relied on using maximum likelihood trees and the maximum parsimony reconciliation thereof to count duplication events on specific branches of interest in a reference species tree. Such approaches do not account for uncertainty in the gene tree and reconciliation, or do so only heuristically. The effects of these simplifications on the inference of ancient WGDs are unclear. In particular, the effects of variation in gene duplication and loss rates across the species tree have not been considered. Here, we developed a full probabilistic approach for phylogenomic reconciliation-based WGD inference, accounting for both gene tree and reconciliation uncertainty using a method based on the principle of amalgamated likelihood estimation. The model and methods are implemented in a maximum likelihood and Bayesian setting and account for variation of duplication and loss rates across the species tree, using methods inspired by phylogenetic divergence time estimation. We applied our newly developed framework to ancient WGDs in land plants and investigated the effects of duplication and loss rate variation on reconciliation and gene count based assessment of these earlier proposed WGDs.

scholarly journals DISCO: Species Tree Inference using Multicopy Gene Family Tree Decomposition

2021 ◽  
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.]

scholarly journals Ancient whole genome duplications and the evolution of the gene duplication and loss rate

2019 ◽  
Author(s):  
Arthur Zwaenepoel ◽  
Yves Van de Peer
Keyword(s):  
Maximum Likelihood ◽  
Gene Duplication ◽  
Loss Rate ◽  
Gene Tree ◽  
Probabilistic Approach ◽  
Species Tree ◽  
Rate Variation ◽  
Whole Genome ◽  
Tree Reconciliation ◽  
Gene Duplication And Loss

AbstractGene tree - species tree reconciliation methods have been employed for studying ancient whole genome duplication (WGD) events across the eukaryotic tree of life. Most approaches have relied on using maximum likelihood trees and the maximum parsimony reconciliation thereof to count duplication events on specific branches of interest in a reference species tree. Such approaches do not account for uncertainty in the gene tree and reconciliation, or do so only heuristically. The effects of these simplifications on the inference of ancient WGDs are unclear. In particular the effects of variation in gene duplication and loss rates across the species tree have not been considered. Here, we developed a full probabilistic approach for phylogenomic reconciliation based WGD inference, accounting for both gene tree and reconciliation uncertainty using a method based on the principle of amalgamated likelihood estimation. The model and methods are implemented in a maximum likelihood and Bayesian setting and account for variation of duplication and loss rate across the species tree, using methods inspired by phylogenetic divergence time estimation. We applied our newly developed framework to ancient WGDs in land plants and investigate the effects of duplication and loss rate variation on reconciliation and gene count based assessment of these earlier proposed WGDs.

scholarly journals Polynomial-Time Statistical Estimation of Species Trees under Gene Duplication and Loss

2019 ◽  
Author(s):  
Brandon Legried ◽  
Erin K. Molloy ◽  
Tandy Warnow ◽  
Sébastien Roch
Keyword(s):  
Gene Duplication ◽  
Polynomial Time ◽  
Incomplete Lineage Sorting ◽  
Data Bank ◽  
Polynomial Time Algorithm ◽  
Species Tree ◽  
Species Trees ◽  
Lineage Sorting ◽  
Biological Studies ◽  
Gene Duplication And Loss

AbstractPhylogenomics—the estimation of species trees from multilocus datasets—is a common step in many biological studies. However, this estimation is challenged by the fact that genes can evolve under processes, including incomplete lineage sorting (ILS) and gene duplication and loss (GDL), that make their trees different from the species tree. In this paper, we address the challenge of estimating the species tree under GDL. We show that species trees are identifiable under a standard stochastic model for GDL, and that the polynomial-time algorithm ASTRAL-multi, a recent development in the ASTRAL suite of methods, is statistically consistent under this GDL model. We also provide a simulation study evaluating ASTRAL-multi for species tree estimation under GDL. All scripts and datasets used in this study are available on the Illinois Data Bank: https://doi.org/10.13012/B2IDB-2626814_V1.

scholarly journals The Perfect Storm: Gene Tree Estimation Error, Incomplete Lineage Sorting, and Ancient Gene Flow Explain the Most Recalcitrant Ancient Angiosperm Clade, Malpighiales

2020 ◽  
Author(s):  
Liming Cai ◽  
Zhenxiang Xi ◽  
Emily Moriarty Lemmon ◽  
Alan R Lemmon ◽  
Austin Mast ◽  
...  
Keyword(s):  
Gene Flow ◽  
Incomplete Lineage Sorting ◽  
Estimation Error ◽  
Gene Tree ◽  
Species Tree ◽  
Flowering Plant ◽  
Estimation Methods ◽  
Lineage Sorting ◽  
Tree Estimation ◽  
Perfect Storm

Abstract The genomic revolution offers renewed hope of resolving rapid radiations in the Tree of Life. The development of the multispecies coalescent (MSC) model and improved gene tree estimation methods can better accommodate gene tree heterogeneity caused by incomplete lineage sorting (ILS) and gene tree estimation error stemming from the short internal branches. However, the relative influence of these factors in species tree inference is not well understood. Using anchored hybrid enrichment, we generated a data set including 423 single-copy loci from 64 taxa representing 39 families to infer the species tree of the flowering plant order Malpighiales. This order includes nine of the top ten most unstable nodes in angiosperms, which have been hypothesized to arise from the rapid radiation during the Cretaceous. Here, we show that coalescent-based methods do not resolve the backbone of Malpighiales and concatenation methods yield inconsistent estimations, providing evidence that gene tree heterogeneity is high in this clade. Despite high levels of ILS and gene tree estimation error, our simulations demonstrate that these two factors alone are insufficient to explain the lack of resolution in this order. To explore this further, we examined triplet frequencies among empirical gene trees and discovered some of them deviated significantly from those attributed to ILS and estimation error, suggesting gene flow as an additional and previously unappreciated phenomenon promoting gene tree variation in Malpighiales. Finally, we applied a novel method to quantify the relative contribution of these three primary sources of gene tree heterogeneity and demonstrated that ILS, gene tree estimation error, and gene flow contributed to 10.0%, 34.8%, and 21.4% of the variation, respectively. Together, our results suggest that a perfect storm of factors likely influence this lack of resolution, and further indicate that recalcitrant phylogenetic relationships like the backbone of Malpighiales may be better represented as phylogenetic networks. Thus, reducing such groups solely to existing models that adhere strictly to bifurcating trees greatly oversimplifies reality, and obscures our ability to more clearly discern the process of evolution.

A Multi-objective Metaheuristic Approach for Accurate Species Tree Estimation

2020 ◽  
Author(s):  
Muhammad Ali Nayeem ◽  
Md. Shamsuzzoha Bayzid ◽  
Sakshar Chakravarty ◽  
Mohammad Saifur Rahman ◽  
M. Sohel Rahman
Keyword(s):  
Species Tree ◽  
Multi Objective ◽  
Tree Estimation

scholarly journals TreeMerge: a new method for improving the scalability of species tree estimation methods

2019 ◽  
Vol 35 (14) ◽  
pp. i417-i426 ◽  
Author(s):  
Erin K Molloy ◽  
Tandy Warnow
Keyword(s):  
Large Scale ◽  
Species Tree ◽  
New Method ◽  
Divide And Conquer ◽  
Supplementary Information ◽  
Estimation Methods ◽  
Running Time ◽  
Tree Estimation ◽  
Computationally Intensive ◽  
A Minor

Abstract Motivation At RECOMB-CG 2018, we presented NJMerge and showed that it could be used within a divide-and-conquer framework to scale computationally intensive methods for species tree estimation to larger datasets. However, NJMerge has two significant limitations: it can fail to return a tree and, when used within the proposed divide-and-conquer framework, has O(n5) running time for datasets with n species. Results Here we present a new method called ‘TreeMerge’ that improves on NJMerge in two ways: it is guaranteed to return a tree and it has dramatically faster running time within the same divide-and-conquer framework—only O(n2) time. We use a simulation study to evaluate TreeMerge in the context of multi-locus species tree estimation with two leading methods, ASTRAL-III and RAxML. We find that the divide-and-conquer framework using TreeMerge has a minor impact on species tree accuracy, dramatically reduces running time, and enables both ASTRAL-III and RAxML to complete on datasets (that they would otherwise fail on), when given 64 GB of memory and 48 h maximum running time. Thus, TreeMerge is a step toward a larger vision of enabling researchers with limited computational resources to perform large-scale species tree estimation, which we call Phylogenomics for All. Availability and implementation TreeMerge is publicly available on Github (http://github.com/ekmolloy/treemerge). Supplementary information Supplementary data are available at Bioinformatics online.

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