scholarly journals Maximum Parsimony Inference of Phylogenetic Networks in the Presence of Polyploid Complexes

2021 ◽  
Author(s):  
Zhi Yan ◽  
Zhen Cao ◽  
Yushu Liu ◽  
Huw A Ogilvie ◽  
Luay Nakhleh
Keyword(s):  
Network Inference ◽  
Incomplete Lineage Sorting ◽  
Gene Tree ◽  
Phylogenetic Network ◽  
Biological Data ◽  
Phylogenetic Networks ◽  
Data Sets ◽  
Polyploid Species ◽  
Lineage Sorting ◽  
Work Done

Abstract Phylogenetic networks provide a powerful framework for modeling and analyzing reticulate evolutionary histories. While polyploidy has been shown to be prevalent not only in plants but also in other groups of eukaryotic species, most work done thus far on phylogenetic network inference assumes diploid hybridization. These inference methods have been applied, with varying degrees of success, to data sets with polyploid species, even though polyploidy violates the mathematical assumptions underlying these methods. Statistical methods were developed recently for handling specific types of polyploids and so were parsimony methods that could handle polyploidy more generally yet while excluding processes such as incomplete lineage sorting. In this paper, we introduce a new method for inferring most parsimonious phylogenetic networks on data that include polyploid species. Taking gene tree topologies as input, the method seeks a phylogenetic network that minimizes deep coalescences while accounting for polyploidy. We demonstrate the performance of the method on both simulated and biological data. The inference method as well as a method for evaluating evolutionary hypotheses in the form of phylogenetic networks are implemented and publicly available in the PhyloNet software package.

scholarly journals Maximum Parsimony Inference of Phylogenetic Networks in the Presence of Polyploid Complexes

2020 ◽  
Author(s):  
Zhi Yan ◽  
Zhen Cao ◽  
Yushu Liu ◽  
Luay Nakhleh
Keyword(s):  
Network Inference ◽  
Incomplete Lineage Sorting ◽  
Phylogenetic Network ◽  
Biological Data ◽  
Phylogenetic Networks ◽  
Data Sets ◽  
Gene Trees ◽  
Polyploid Species ◽  
Lineage Sorting ◽  
Work Done

AbstractPhylogenetic networks provide a powerful framework for modeling and analyzing reticulate evolutionary histories. While polyploidy has been shown to be prevalent not only in plants but also in other groups of eukaryotic species, most work done thus far on phylogenetic network inference assumes diploid hybridization. These inference methods have been applied, with varying degrees of success, to data sets with polyploid species, even though polyploidy violates the mathematical assumptions underlying these methods. Statistical methods were developed recently for handling specific types of polyploids and so were parsimony methods that could handle polyploidy more generally yet while excluding processes such as incomplete lineage sorting. In this paper, we introduce a new method for inferring most parsimonious phylogenetic networks on data that include polyploid species. Taking gene trees as input, the method seeks a phylogenetic network that minimizes deep coalescences while accounting for polyploidy. The method could also infer trees, thus potentially distinguishing between auto- and allo-polyploidy. We demonstrate the performance of the method on both simulated and biological data. The inference method as well as a method for evaluating given phylogenetic networks are implemented and publicly available in the PhyloNet software package.

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 A Divide-and-Conquer Method for Scalable Phylogenetic Network Inference from Multi-locus Data

2019 ◽  
Author(s):  
Jiafan Zhu ◽  
Xinhao Liu ◽  
Huw A. Ogilvie ◽  
Luay K. Nakhleh
Keyword(s):  
Large Scale ◽  
Network Inference ◽  
Incomplete Lineage Sorting ◽  
Phylogenetic Network ◽  
Biological Data ◽  
Phylogenetic Networks ◽  
Divide And Conquer ◽  
Lineage Sorting ◽  
Step Method ◽  
Sequence Alignments

AbstractReticulate evolutionary histories, such as those arising in the presence of hybridization, are best modeled as phylogenetic networks. Recently developed methods allow for statistical inference of phylogenetic networks while also accounting for other processes, such as incomplete lineage sorting (ILS). However, these methods can only handle a small number of loci from a handful of genomes.In this paper, we introduce a novel two-step method for scalable inference of phylogenetic networks from the sequence alignments of multiple, unlinked loci. The method infers networks on subproblems and then merges them into a network on the full set of taxa. To reduce the number of trinets to infer, we formulate a Hitting Set version of the problem of finding a small number of subsets, and implement a simple heuristic to solve it. We studied their performance, in terms of both running time and accuracy, on simulated as well as on biological data sets. The two-step method accurately infers phylogenetic networks at a scale that is infeasible with existing methods. The results are a significant and promising step towards accurate, large-scale phylogenetic network inference.We implemented the algorithms in the publicly available software package PhyloNet (https://bioinfocs.rice.edu/PhyloNet)[email protected]

scholarly journals Unifying Gene Duplication, Loss, and Coalescence on Phylogenetic Networks

2019 ◽  
Author(s):  
Peng Du ◽  
Huw A. Ogilvie ◽  
Luay Nakhleh
Keyword(s):  
Gene Duplication ◽  
Incomplete Lineage Sorting ◽  
Gene Tree ◽  
Phylogenetic Network ◽  
Biological Data ◽  
Phylogenetic Networks ◽  
Lineage Sorting ◽  
Evolutionary Processes ◽  
Domains Of Life ◽  
Gene Duplication And Loss

AbstractStatistical methods were recently introduced for inferring phylogenetic networks under the multispecies network coalescent, thus accounting for both reticulation and incomplete lineage sorting. Two evolutionary processes that are ubiquitous across all three domains of life, but are not accounted for by those methods, are gene duplication and loss (GDL).In this work, we devise a three-piece model—phylogenetic network, locus network, and gene tree—that unifies all the aforementioned processes into a single model of how genes evolve in the presence of ILS, GDL, and introgression within the branches of a phylogenetic network. To illustrate the power of this model, we develop an algorithm for estimating the parameters of a phylogenetic network topology under this unified model. The algorithm consists of a set of moves that allow for stochastic search through the parameter space. The challenges with developing such moves stem from the intricate dependencies among the three pieces of the model. We demonstrate the application of the model and the accuracy of the algorithm on simulated as well as biological data.Our work adds to the biologist’s toolbox of methods for phylogenomic inference by accounting for more complex evolutionary processes.

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

2019 ◽  
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 ◽  
Molecular Data ◽  
Species Trees ◽  
Lineage Sorting ◽  
Multiple Sources ◽  
Gene Tree Discordance ◽  
Phylogenomic Analyses

AbstractGene tree discordance in large genomic datasets 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 dataset 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.

scholarly journals NetRAX: Accurate and Fast Maximum Likelihood Phylogenetic Network Inference⋆

2021 ◽  
Author(s):  
Sarah Lutteropp ◽  
Céline Scornavacca ◽  
Alexey M. Kozlov ◽  
Benoit Morel ◽  
Alexandros Stamatakis
Keyword(s):  
Maximum Likelihood ◽  
Network Inference ◽  
Likelihood Function ◽  
Incomplete Lineage Sorting ◽  
Phylogenetic Network ◽  
Phylogenetic Networks ◽  
Small Data ◽  
Lineage Sorting ◽  
Sequence Alignments ◽  
Multiple Sequence

AbstractPhylogenetic networks are used to represent non-treelike evolutionary scenarios. Current, actively developed approaches for phylogenetic network inference jointly account for non-treelike evolution and incomplete lineage sorting (ILS). Unfortunately, this induces a very high computational complexity. Hence, current tools can only analyze small data sets.We present NetRAX, a tool for maximum likelihood inference of phylogenetic networks in the absence of incomplete lineage sorting. Our tool leverages state-of-the-art methods for efficiently computing the phylogenetic likelihood function on trees, and extends them to phylogenetic networks via the notion of “displayed trees”. NetRAX can infer maximum likelihood phylogenetic networks from partitioned multiple sequence alignments and returns the inferred networks in Extended Newick format.On simulated data, our results show a very low relative difference in BIC score and a near-zero unrooted softwired cluster distance to the true, simulated networks. With NetRAX, a network inference on a partitioned alignment with 8, 000 sites, 30 taxa, and 3 reticulations completes within a few minutes on a standard laptop.Our implementation is available under the GNU General Public License v3.0 at https://github.com/lutteropp/NetRAX.

scholarly journals Phylogenetic Trees and Networks Can Serve as Powerful and Complementary Approaches for Analysis of Genomic Data

2019 ◽  
Vol 69 (3) ◽  
pp. 593-601 ◽  
Author(s):  
Christopher Blair ◽  
Cécile Ané
Keyword(s):  
Gene Flow ◽  
Phylogenetic Trees ◽  
Evolutionary History ◽  
Incomplete Lineage Sorting ◽  
Gene Tree ◽  
Phylogenetic Network ◽  
Genomic Data ◽  
Point Of View ◽  
Phylogenetic Networks ◽  
Lineage Sorting

Abstract Genomic data have had a profound impact on nearly every biological discipline. In systematics and phylogenetics, the thousands of loci that are now being sequenced can be analyzed under the multispecies coalescent model (MSC) to explicitly account for gene tree discordance due to incomplete lineage sorting (ILS). However, the MSC assumes no gene flow post divergence, calling for additional methods that can accommodate this limitation. Explicit phylogenetic network methods have emerged, which can simultaneously account for ILS and gene flow by representing evolutionary history as a directed acyclic graph. In this point of view, we highlight some of the strengths and limitations of phylogenetic networks and argue that tree-based inference should not be blindly abandoned in favor of networks simply because they represent more parameter rich models. Attention should be given to model selection of reticulation complexity, and the most robust conclusions regarding evolutionary history are likely obtained when combining tree- and network-based inference.

scholarly journals A divide-and-conquer method for scalable phylogenetic network inference from multilocus data

2019 ◽  
Vol 35 (14) ◽  
pp. i370-i378 ◽  
Author(s):  
Jiafan Zhu ◽  
Xinhao Liu ◽  
Huw A Ogilvie ◽  
Luay K Nakhleh
Keyword(s):  
Large Scale ◽  
Network Inference ◽  
Incomplete Lineage Sorting ◽  
Phylogenetic Network ◽  
Phylogenetic Networks ◽  
Divide And Conquer ◽  
Supplementary Information ◽  
Lineage Sorting ◽  
Step Method ◽  
Sequence Alignments

Abstract Motivation Reticulate evolutionary histories, such as those arising in the presence of hybridization, are best modeled as phylogenetic networks. Recently developed methods allow for statistical inference of phylogenetic networks while also accounting for other processes, such as incomplete lineage sorting. However, these methods can only handle a small number of loci from a handful of genomes. Results In this article, we introduce a novel two-step method for scalable inference of phylogenetic networks from the sequence alignments of multiple, unlinked loci. The method infers networks on subproblems and then merges them into a network on the full set of taxa. To reduce the number of trinets to infer, we formulate a Hitting Set version of the problem of finding a small number of subsets, and implement a simple heuristic to solve it. We studied their performance, in terms of both running time and accuracy, on simulated as well as on biological datasets. The two-step method accurately infers phylogenetic networks at a scale that is infeasible with existing methods. The results are a significant and promising step towards accurate, large-scale phylogenetic network inference. Availability and implementation We implemented the algorithms in the publicly available software package PhyloNet (https://bioinfocs.rice.edu/PhyloNet). Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals DGEN: A Test Statistic for Detection of General Introgression Scenarios

2018 ◽  
Author(s):  
Ryan A. Leo Elworth ◽  
Chabrielle Allen ◽  
Travis Benedict ◽  
Peter Dulworth ◽  
Luay Nakhleh
Keyword(s):  
Evolutionary Biology ◽  
Incomplete Lineage Sorting ◽  
Gene Tree ◽  
Genetic Material ◽  
Phylogenetic Network ◽  
Data Sets ◽  
Lineage Sorting ◽  
Test Statistic ◽  
Tree Model ◽  
Data Set

AbstractWhen two species hybridize, one outcome is the integration of genetic material from one species into the genome of the other, a process known as introgression. Detecting introgression in genomic data is a very important question in evolutionary biology. However, given that hybridization occurs between closely related species, a compli-cating factor for introgression detection is the presence of incomplete lineage sorting, or ILS. The D-statistic, famously referred to as the “ABBA-BABA” test, was pro-posed for introgression detection in the presence of ILS in data sets that consist of four genomes. More recently, DFOIL—a set of statistics—was introduced to extend the D-statistic to data sets of five genomes.The major contribution of this paper is demonstrating that the invariants underly-ing both the D-statistic and DFOIL can be derived automatically from the probability mass functions of gene tree topologies under the null species tree model and alterna-tive phylogenetic network model. Computational requirements aside, this automatic derivation provides a way to generalize these statistics to data sets of any size and with any scenarios of introgression. We demonstrate the accuracy of the general statistic, which we call DGEN, on simulated data sets with varying rates of introgression, and apply it to an empirical data set of mosquito genomes.We have implemented DGEN and made it available, both as a graphical user interface tool and as a command-line tool, as part of the freely available, open-source software package ALPHA (https://github.com/chilleo/ALPHA).

scholarly journals Inferring Phylogenetic Networks Using PhyloNet

2017 ◽  
Author(s):  
Dingqiao Wen ◽  
Yun Yu ◽  
Jiafan Zhu ◽  
Luay Nakhleh
Keyword(s):  
Bayesian Inference ◽  
Maximum Likelihood ◽  
Maximum Parsimony ◽  
Software Package ◽  
Incomplete Lineage Sorting ◽  
Gene Tree ◽  
Phylogenetic Network ◽  
Phylogenetic Networks ◽  
Lineage Sorting ◽  
Sequence Alignments

AbstractPhyloNet was released in 2008 as a software package for representing and analyzing phylogenetic networks. At the time of its release, the main functionalities in PhyloNet consisted of measures for comparing network topologies and a single heuristic for reconciling gene trees with a species tree. Since then, PhyloNet has grown significantly. The software package now includes a wide array of methods for inferring phylogenetic networks from data sets of unlinked loci while accounting for both reticulation (e.g., hybridization) and incomplete lineage sorting. In particular, PhyloNet now allows for maximum parsimony, maximum likelihood, and Bayesian inference of phylogenetic networks from gene tree estimates. Furthermore, Bayesian inference directly from sequence data (sequence alignments or bi-allelic markers) is implemented. Maximum parsimony is based on an extension of the “minimizing deep coalescences” criterion to phylogenetic networks, whereas maximum likelihood and Bayesian inference are based on the multispecies network coalescent. All methods allow for multiple individuals per species. As computing the likelihood of a phylogenetic network is computationally hard, PhyloNet allows for evaluation and inference of networks using a pseudo-likelihood measure. PhyloNet summarizes the results of the various analyses, and generates phylogenetic networks in the extended Newick format that is readily viewable by existing visualization software, [phylogenetic networks; reticulation; incomplete lineage sorting; multispecies network coalescent; Bayesian inference; maximum likelihood; maximum parsimony.]

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