wQFM: Statistically Consistent Genome-scale Species Tree Estimation from Weighted Quartets

Mahbub, M.; Wahab, Zahin; Reaz, Rezwana; Rahman, Mizanur; Bayzid, Md. Shamsuzzoha

doi:10.1101/2020.11.30.403352

2020

DOI: 10.1101/2020.11.30.403352

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wQFM: Statistically Consistent Genome-scale Species Tree Estimation from Weighted Quartets

M. Mahbub

Zahin Wahab

Rezwana Reaz

et al.

Abstract: Motivation: Species tree estimation from genes sampled from throughout the whole genome is complicated due to the gene tree-species tree discordance. Incomplete lineage sorting (ILS) is one of the most frequent causes for this discordance, where alleles can coexist in populations for periods that may span several speciation events. Quartet-based summary methods for estimating species trees from a collection of gene trees are becoming popular due to their high accuracy and statistical guarantee under ILS. Gener… Show more

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Cited by 3 publications

References 88 publications

(75 reference statements)

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QT-GILD: Quartet based gene tree imputation using deep learning improves phylogenomic analyses despite missing data

Mahbub

Sawmya

Saha

et al. 2021

Preprint

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Species tree estimation is frequently based on phylogenomic approaches that use multiple genes from throughout the genome. However, for a combination of reasons (ranging from sampling biases to more biological causes, as in gene birth and loss), gene trees are often incomplete, meaning that not all species of interest have a common set of genes. Incomplete gene trees can potentially impact the accuracy of phylogenomic inference. We, for the first time, introduce the problem of imputing the quartet distribution induced by a set of incomplete gene trees, which involves adding the missing quartets back to the quartet distribution. We present QT-GILD, an automated and specially tailored unsupervised deep learning technique, accompanied by cues from natural language processing (NLP), which learns the quartet distribution in a given set of incomplete gene trees and generates a complete set of quartets accordingly. QT-GILD is a general-purpose technique needing no explicit modeling of the subject system or reasons for missing data or gene tree heterogeneity. Experimental studies on a collection of simulated and empirical data sets suggest that QT-GILD can effectively impute the quartet distribution, which results in a dramatic improvement in the species tree accuracy. Remarkably, QT-GILD not only imputes the missing quartets but it can also account for gene tree estimation error. Therefore, QT-GILD advances the state-of-the-art in species tree estimation from gene trees in the face of missing data. QT-GILD is freely available in open source form at https://github.com/pythonLoader/QT-GILD.

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QT-GILD: Quartet based gene tree imputation using deep learning improves phylogenomic analyses despite missing data

Mahbub

Sawmya

Saha

et al. 2021

Preprint

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TREE-QMC: Improving quartet graph construction for scalable and accurate species tree estimation from gene trees

Han

Molloy

2022

Preprint

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Genome-scale data is increasingly available for species tree estimation, bringing attention to the fact that different regions of the genome can have evolutionary histories that differ from each other and from the species tree due to incomplete lineage sorting. This has led to the development of many new methods, some of which estimate the species tree from a collection of (estimated) gene trees. ASTRAL, the dominant method in this class, executes an exact algorithm for the Maximum Quartet Support Species Tree problem within a constrained solution space, constructed from the input gene trees. The other popular heuristics for this NP-hard problem, wQMC and wQFM, take a set of weighted quartets as input and construct the species tree in a divide-and-conquer fashion, for example via graph cuts. This approach has failed to compete with ASTRAL because it is less scalable when considering the time required to explicitly weight quartets based on the gene trees. Moreover, in prior studies, wQMC has been consistently less accurate than either wQFM or ASTRAL.Here, we address that the poor accuracy of wQMC by showing that the quartet graph should be normalized to account for “artificial taxa,” which are introduced during the divide phase so that solutions on subproblems can be combined during the conquer phase. This led us to design efficient techniques for uniform and non-uniform graph normalization, with the goal of the latter being improved robustness on data sets with large numbers of taxa and gene tree heterogeneity. We provide an algorithm to construct the normalized quartet graph directly from the input gene trees, enabling our new method TREE-QMC to run in O(n3k) time where n is the number of species and k is the number of gene trees (provided some assumptions on subproblem sizes). In an extensive simulation study, graph normalization enabled TREE-QMC to be competitive in terms of species tree accuracy with ASTRAL-III and FASTRAL, a recent method that runs ASTRAL-III using an aggressively constrained solution space to speedup analyses. Notably, when the number of taxa was large (> 500), TREE-QMC (with non-uniform graph normalization) produced more accurate species trees than either FASTRAL and ASTRAL-III, at speeds closer to FASTRAL. We also re-analyzed an avian data set of 3,679 ultraconserved elements, finding that the estimated trees differed only on the short branches suggestive of ILS, with the TREE-QMC tree being closer to the concatenation tree than the ASTRAL-III/FASTRAL tree. Overall, our study shows that wQMC’s divide-and-conquer framework can be competitive with the dynamic programming approach of ASTRAL.

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QT-WEAVER: Correcting quartet distribution improves phylogenomic analyses despite gene tree estimation error

Bin Hasan,

Sohaib,

Bayzid

2024

Preprint

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Summarizing individual gene trees into species phylogenies using coalescent-based methods has become a standard approach in phylogenomics. However, gene tree estimation error (GTEE) arising from a combination of reasons (ranging from analytical factors to more biological causes, as in short gene sequences) can potentially impact the accuracy of phylogenomic inference. We, for the first time, introduce the problem of correcting the quartet distribution induced by a set of estimated gene trees, which involves updating the weights of the quartets to better reflect their relative importance within the gene tree distribution. We present QT-WEAVER, the first method of its kind, which learns the conflicts within the quartet distribution induced by a given set of gene trees and generates an updated quartet distribution by adjusting the weights accordingly. QT-WEAVER is a general-purpose technique needing no explicit modeling of the subject system or reasons for GTEE or gene tree heterogeneity. Experimental studies on a collection of simulated and empirical data sets suggest that QT-WEAVER can effectively account for GTEE, which results in a substantial improvement in the species tree accuracy. Additionally, the concept of quartet conflicts and related algorithmic and combinatorial innovations introduced in this study will benefit various quartet-based computations. Therefore, QT-WEAVER advances the state-of-the-art in species tree estimation from gene trees in the face of GTEE. QT-WEAVER is freely available in open-source form at https://github.com/navidh86/QT-WEAVER.

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wQFM: Statistically Consistent Genome-scale Species Tree Estimation from Weighted Quartets

Cited by 3 publications

References 88 publications

QT-GILD: Quartet based gene tree imputation using deep learning improves phylogenomic analyses despite missing data

QT-GILD: Quartet based gene tree imputation using deep learning improves phylogenomic analyses despite missing data

TREE-QMC: Improving quartet graph construction for scalable and accurate species tree estimation from gene trees

QT-WEAVER: Correcting quartet distribution improves phylogenomic analyses despite gene tree estimation error

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