Likelihood-based tree reconstruction on a concatenation of aligned sequence data sets can be statistically inconsistent

Roch, Sébastien; Steel, Mike

doi:10.1016/j.tpb.2014.12.005

Cited by 239 publications

(183 citation statements)

References 33 publications

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“…However, the performance of the gene-tree-based coalescent methods can be improved by increasing the number of genes. In contrast, the concatenation analyses may consistently estimate wrong species trees when the internal branches of species trees are short (Roch & Steel, 2015). The poor performance of the concatenation method cannot be improved by increasing the number of genes.…”

Section: Coalescent Versus Concatenationmentioning

confidence: 97%

“…We find that the coalescent-based methods perform well, regardless of the degree of incomplete lineage sorting (ILS). In contrast, high ILS may positively mislead the concatenation method (Kubatko & Degnan, 2007;Roch & Steel, 2015). In the presence of a high degree of deep coalescence and mutation, the coalescent methods can accurately estimate the species tree with a high probability, when there are a large number of genes.…”

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confidence: 99%

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Coalescent methods for estimating species trees from phylogenomic data

Liu

2015

J of Sytematics Evolution

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Genome-scale sequence data have become increasingly available in the phylogenetic studies for understanding the evolutionary histories of species. However, it is challenging to develop probabilistic models to account for heterogeneity of phylogenomic data. The multispecies coalescent model describes gene trees as independent random variables generated from a coalescence process occurring along the lineages of the species tree. Since the multispecies coalescent model allows gene trees to vary across genes, coalescent-based methods have been popularly used to account for heterogeneous gene trees in phylogenomic data analysis. In this paper, we summarize and evaluate the performance of coalescent-based methods for estimating species trees from genomescale sequence data. We investigate the effects of deep coalescence and mutation on the performance of species tree estimation methods. We found that the coalescent-based methods perform well in estimating species trees for a large number of genes, regardless of the degree of deep coalescence and mutation. The performance of the coalescent methods is negatively correlated with the lengths of internal branches of the species tree.

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Section: Coalescent Versus Concatenationmentioning

confidence: 97%

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confidence: 99%

Coalescent methods for estimating species trees from phylogenomic data

Liu

2015

J of Sytematics Evolution

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“…In other words, the anomaly zone is the inconsistency zone for the majority-vote method. Computer simulation (Kubatko and Degnan 2007) and mathematical analysis (Roch and Steel 2015) suggest that concatenation may similarly be inconsistent in certain regions of the parameter space; concatenation has its own anomaly or inconsistency zone.…”

Section: The Anomaly Zonementioning

confidence: 99%

Challenges in Species Tree Estimation Under the Multispecies Coalescent Model

2016

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The multispecies coalescent (MSC) model has emerged as a powerful framework for inferring species phylogenies while accounting for ancestral polymorphism and gene tree-species tree conflict. A number of methods have been developed in the past few years to estimate the species tree under the MSC. The full likelihood methods (including maximum likelihood and Bayesian inference) average over the unknown gene trees and accommodate their uncertainties properly but involve intensive computation. The approximate or summary coalescent methods are computationally fast and are applicable to genomic datasets with thousands of loci, but do not make an efficient use of information in the multilocus data. Most of them take the two-step approach of reconstructing the gene trees for multiple loci by phylogenetic methods and then treating the estimated gene trees as observed data, without accounting for their uncertainties appropriately. In this article we review the statistical nature of the species tree estimation problem under the MSC, and explore the conceptual issues and challenges of species tree estimation by focusing mainly on simple cases of three or four closely related species. We use mathematical analysis and computer simulation to demonstrate that large differences in statistical performance may exist between the two classes of methods. We illustrate that several counterintuitive behaviors may occur with the summary methods but they are due to inefficient use of information in the data by summary methods and vanish when the data are analyzed using full-likelihood methods. These include (i) unidentifiability of parameters in the model, (ii) inconsistency in the so-called anomaly zone, (iii) singularity on the likelihood surface, and (iv) deterioration of performance upon addition of more data. We discuss the challenges and strategies of species tree inference for distantly related species when the molecular clock is violated, and highlight the need for improving the computational efficiency and model realism of the likelihood methods as well as the statistical efficiency of the summary methods.

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“…Several variations of the binary tree structure have been conceived, such as binary search trees, red-black trees [10], AVL trees [1], B-trees [3], and so on. Binary trees are often used as auxiliary data structures in other research endeavors, both practical (e.g., [18,15]) and theoretical (e.g., [16,14]), but occasionally are the subject of the research itself (e.g., [2]). …”

Section: Introductionmentioning

confidence: 99%

Space Complexity Analysis of the Binary Tree Roll Algorithm

Božinovski

Tanev

Stojcevska

et al. 2017

JITA

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This paper presents the space complexity analysis of the Binary Tree Roll algorithm. The space complexity is analyzed theoretically and the results are then confi rmed empirically. The theoretical analysis consists of determining the amount of memory occupied during the execution of the algorithm and deriving functions of it, in terms of the number of nodes of the tree n, for the worst -and best-case scenarios. The empirical analysis of the space complexity consists of measuring the maximum and minimum amounts of memory occupied during the execution of the algorithm, for all binary tree topologies with the given number of nodes. The space complexity is shown, both theoretically and empirically, to be logarithmic in the best case and linear in the worst case, whereas its average case is shown to be dominantly logarithmic.

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Likelihood-based tree reconstruction on a concatenation of aligned sequence data sets can be statistically inconsistent

Cited by 239 publications

References 33 publications

Coalescent methods for estimating species trees from phylogenomic data

Coalescent methods for estimating species trees from phylogenomic data

Challenges in Species Tree Estimation Under the Multispecies Coalescent Model

Space Complexity Analysis of the Binary Tree Roll Algorithm

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