scholarly journals Species Tree Inference from Genomic Sequences Using the Log-Det Distance

2019 ◽  
Vol 3 (1) ◽  
pp. 107-127 ◽  
Author(s):  
Elizabeth S. Allman ◽  
Colby Long ◽  
John A. Rhodes
Keyword(s):  
Species Tree ◽  
Genomic Sequences ◽  
Tree Inference ◽  
Species Tree Inference

scholarly journals Species Tree Inference with BPP Using Genomic Sequences and the Multispecies Coalescent

2018 ◽  
Vol 35 (10) ◽  
pp. 2585-2593 ◽  
Author(s):  
Tomáš Flouri ◽  
Xiyun Jiao ◽  
Bruce Rannala ◽  
Ziheng Yang
Keyword(s):  
Species Tree ◽  
Genomic Sequences ◽  
Multispecies Coalescent ◽  
Tree Inference ◽  
Species Tree Inference

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 Gene tree correction for reconciliation and species tree inference: Complexity and algorithms

2014 ◽  
Vol 25 ◽  
pp. 51-65 ◽  
Author(s):  
Riccardo Dondi ◽  
Nadia El-Mabrouk ◽  
Krister M. Swenson
Keyword(s):  
Gene Tree ◽  
Species Tree ◽  
Tree Inference ◽  
Species Tree Inference

scholarly journals THE INFLUENCE OF SAMPLING DESIGN ON SPECIES TREE INFERENCE: A NEW RELATIONSHIP FOR THE NEW WORLD CHICKADEES (AVES:POECILE)

Evolution ◽  
2013 ◽  
Vol 68 (2) ◽  
pp. 501-513 ◽  
Author(s):  
Rebecca B. Harris ◽  
Matthew D. Carling ◽  
Irby J. Lovette
Keyword(s):  
New World ◽  
Sampling Design ◽  
Species Tree ◽  
Tree Inference ◽  
Species Tree Inference

scholarly journals Phylogeny of the cycads based on multiple single-copy nuclear genes: congruence of concatenated parsimony, likelihood and species tree inference methods

2013 ◽  
Vol 112 (7) ◽  
pp. 1263-1278 ◽  
Author(s):  
Dayana E. Salas-Leiva ◽  
Alan W. Meerow ◽  
Michael Calonje ◽  
M. Patrick Griffith ◽  
Javier Francisco-Ortega ◽  
...  
Keyword(s):  
Single Copy ◽  
Species Tree ◽  
Nuclear Genes ◽  
Tree Inference ◽  
Inference Methods ◽  
Species Tree Inference

scholarly journals Phylogenetic conflicts, combinability, and deep phylogenomics in plants

2018 ◽  
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.

scholarly journals The Diverse Applications of Tree Set Visualization and Exploration

2020 ◽  
Author(s):  
Jeremy M. Brown ◽  
Genevieve G. Mount ◽  
Kyle A. Gallivan ◽  
James Wilgenbusch
Keyword(s):  
Phylogenetic Tree ◽  
Software Package ◽  
Species Tree ◽  
Model Based ◽  
Phylogenetic Studies ◽  
Tree Inference ◽  
Visualization Techniques ◽  
Species Tree Inference

All phylogenetic studies are built around sets of trees. Tree sets carry different kinds of information depending on the data and approaches used to generate them, but ultimately the variation they contain and their structure is what drives new phylogenetic insights. In order to better understand the variation in and structure of phylogenetic tree sets, we need tools that are generic, flexible, and exploratory. These tools can serve as natural complements to more formal, statistical investigations and allow us to flag surprising or unexpected observations, better understand the results of model-based studies, as well as build intuition. Here, we describe such a set of tools and provide examples of how they can be applied to relevant questions in phylogenetics, phylogenomics, and species-tree inference. These tools include both visualization techniques and quantitative summaries and are currently implemented in the TreeScaper software package (Huang et al. 2016).

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

2018 ◽  
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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