<i>SimPhy</i>: Phylogenomic Simulation of Gene, Locus, and Species Trees

Mallo, Diego; Martins, Leonardo de Oliveira; Posada, David

doi:10.1093/sysbio/syv082

Cited by 150 publications

(102 citation statements)

References 54 publications

Supporting

Mentioning

102

Contrasting

Order By: Relevance

“…We evaluated methods on simulated datasets based on the 16-taxon fungal dataset studied in [10,25]. We used this fungal dataset to construct a model species tree, on which we generated gene trees using SimPhy [20] under three GDL rates (the lowest of which is the one selected by [25] to reflect the fungal dataset, so that the higher two reflect more challenging conditions). The branch lengths in the gene trees were rescaled by random values to deviate from the strict molecular clock, and then used to generate sequences using Indelible [13] under GTR+GAMMA models selected using these data.…”

Section: Methodsmentioning

confidence: 99%

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

Legried

Molloy

Warnow

et al. 2019

Preprint

View full text Add to dashboard Cite

Phylogenomics-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.

show abstract

Section: Methodsmentioning

confidence: 99%

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

Legried

Molloy

Warnow

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…The CLASSIPHY R package provides a Lrapper function for SimPhy (Mallo et al, 2016), a fast and versatile program that can simulate multiple sources of gene-tree discord. In this study, Le used this program to simulate the tLo processes-ILS and LGT.…”

Section: Classiphy Methodsmentioning

confidence: 99%

Cause of gene tree discord? Distinguishing incomplete lineage sorting and lateral gene transfer in phylogenetics

Huang

Sukumaran

Smith

et al. 2017

Preprint

View full text Add to dashboard Cite

Despite recent efforts that have produced data sets with hundreds and thousands of gene regions to resolve regions of the tree of life, recalcitrant nodes persist and disagreement among genes as well as disagreement between individual gene trees and species trees are common. There are a number of evolutionary processes that contribute to these conflicts between gene trees and species trees, including deep coalescence (lineage sorting), horizontal gene transfer or hybridization, etc. While for some of these processes, we have very powerful and sophisticated models that uses the conflict in the gene trees as information that contributes materially to correctly inferring the species tree, such as the multispecies coalescent (MSC). However, usage of these models require a priori recognition of relevant processes, which is often unknown for empirical dataset. Here we propose a new perspective to not only identify the cause of discord among gene trees, but also use it to classify loci by the underlying cause of discord to identify subsets of loci for analysis with the goal of improving phylogenetic accuracy. This approach differs fundamentally from all other criteria used for making decisions about which loci to include in a phylogenetic analysis. In particular, the choice of loci in this framework is based on identifying those that reflect descent from a common ancestor (as opposed to other processes), and thereby can minimize problems with model misspecification. We present preliminary results that demonstrate the potential of this framework in distinguishing the lateral gene transfer (LGT) from incomplete lineage sorting (ILS) process, as implemented in a new software package CLASSIPHY, while also highlighting areas for further development and testing. We discussed why such methods (i) are critical to improving phylogenetic accuracy with the increased complexity of genomic/transcriptomic datasets, and that (ii) characterizing patterns of discordance and the contribution of different processes to this discordance is itself of interest for generating hypotheses about the role of lateral gene transfer, gene duplication, and incomplete lineage sorting during the divergence of different taxa. correctly inferring the species tree, such as the multispecies coalescent (MSC). HoLever, usage of these models require a priori recognition of relevant processes, Lhich is often unknoLn for empirical dataset. Here Le propose a neL perspective to not only identify the cause of discord among gene trees, but also use it to classify loci by the underlying cause of discord to identify subsets of loci for analysis Lith the goal of improving phylogenetic accuracy. This approach differs fundamentally from all other criteria used for making decisions about Lhich loci to include in a phylogenetic analysis. In particular, the choice of loci in this frameLork is based on identifying those that reflect descent from a common ancestor (as opposed to other processes), and thereby can minimize problems Lith model misspecification. We present prel...

show abstract

“…SimPhy (Mallo et al , 2016) was used to generate gene trees within species trees under the MSC model. By holding the effective population size constant and varying the species tree height (in generations), model conditions with two different levels of ILS were created, each with 20 replicate datasets.…”

Section: Performance Studymentioning

confidence: 99%

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

Molloy

Warnow

2019

Bioinformatics

View full text Add to dashboard Cite

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.

show abstract

SimPhy: Phylogenomic Simulation of Gene, Locus, and Species Trees

Cited by 150 publications

References 54 publications

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

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

Cause of gene tree discord? Distinguishing incomplete lineage sorting and lateral gene transfer in phylogenetics

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

Contact Info

Product

Resources

About