wQFM: highly accurate genome-scale species tree estimation from weighted quartets

Mahbub, M.; Wahab, Zahin; Reaz, Rezwana; Rahman, Mohammad Saifur; Bayzid, Shamsuzzoha

doi:10.1093/bioinformatics/btab428

Cited by 19 publications

(42 citation statements)

References 90 publications

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“… Allman et al (2011) (here given as ADR) provided one of the fundamental theorems underlying species tree estimation under the MSC: they proved that for any four species, the unrooted topology of the species tree is the same as the topology of the most probable unrooted gene tree. This theorem has been used to establish statistical consistency for quartet-based methods, such as ASTRAL, wQFM ( Mahbub et al , 2021 ) and the population tree in BUCKy ( Larget et al , 2010 ). Interestingly, Theorem 9 from ADR has received much less attention and (to the best of our knowledge) is not used in any species tree estimation method.…”

Section: Introductionmentioning

confidence: 99%

Quintet Rooting: rooting species trees under the multi-species coalescent model

2022

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Motivation Rooted species trees are a basic model with multiple applications throughout biology, including understanding adaptation, biodiversity, phylogeography and co-evolution. Because most species tree estimation methods produce unrooted trees, methods for rooting these trees have been developed. However, most rooting methods either rely on prior biological knowledge or assume that evolution is close to clock-like, which is not usually the case. Furthermore, most prior rooting methods do not account for biological processes that create discordance between gene trees and species trees. Results We present Quintet Rooting (QR), a method for rooting species trees based on a proof of identifiability of the rooted species tree under the multi-species coalescent model established by Allman, Degnan and Rhodes (J. Math. Biol., 2011). We show that QR is generally more accurate than other rooting methods, except under extreme levels of gene tree estimation error. Availability and implementation Quintet Rooting is available in open source form at https://github.com/ytabatabaee/Quintet-Rooting. The simulated datasets used in this study are from a prior study and are available at https://www.ideals.illinois.edu/handle/2142/55319. The biological dataset used in this study is also from a prior study and is available at http://gigadb.org/dataset/101041. Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Introductionmentioning

confidence: 99%

Quintet Rooting: rooting species trees under the multi-species coalescent model

2022

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“…Importantly, ASTRAL operates directly on the input set of k gene trees instead of explicitly constructing a set of Ω( n 4 ) weighted quartets, where n is the number of species (also referred to as taxa). This is in stark contrast to other popular heuristics for MQSST, namely Weighted Quartet Max Cut (wQMC; Avni et al, 2014) and Weighted Quartet Fiduccia-Mattheyses (wQFM; Mahbub et al, 2021), which take a set of weighted quartets as input and construct the species tree in a divide-and-conquer fashion, for example via graph cuts. When considering the time to weight quartets based on the gene trees, both wQFM and wQMC can be far more computationally intensive than ASTRAL (Mahbub et al, 2021).…”

Section: Introductionmentioning

confidence: 92%

“…This is in stark contrast to other popular heuristics for MQSST, namely Weighted Quartet Max Cut (wQMC; Avni et al, 2014) and Weighted Quartet Fiduccia-Mattheyses (wQFM; Mahbub et al, 2021), which take a set of weighted quartets as input and construct the species tree in a divide-and-conquer fashion, for example via graph cuts. When considering the time to weight quartets based on the gene trees, both wQFM and wQMC can be far more computationally intensive than ASTRAL (Mahbub et al, 2021). Additionally, wQMC was substantially less accurate than either wQFM or ASTRAL in a recent simulation study (Mahbub et al, 2021).…”

Section: Introductionmentioning

confidence: 92%

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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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“…In recent years, many accurate summary methods statistically consistent under the MSC have been developed, such as MP-EST [20], NJst [37], ASTRAL [26], ASTRID [46], FASTRAL [8], wQFM [23]. Many of these methods are scalable to genomic-scale data, and under sufficient gene signal and ILS tend to be more accurate than concatenation [31].…”

Section: Introductionmentioning

confidence: 99%

Fast and Accurate Species Trees from Weighted Internode Distances

Liu

Warnow

2022

Preprint

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Species tree estimation is a basic step in many biological research projects, but is complicated by the fact that gene trees can differ from the species tree due to processes such as incomplete lineage sorting (ILS), gene duplication and loss (GDL), and horizontal gene transfer (HGT), which can cause different regions within the genome to have different evolutionary histories (i.e., “gene tree heterogeneity”). One approach to estimating species trees in the presence of gene tree heterogeneity resulting from ILS operates by computing trees on each genomic region (i.e., computing “gene trees”) and then using these gene trees to define a matrix of average internode distances, where the internode distance in a tree T between two species x and y is the number of nodes in T between the leaves corresponding to x and y. Given such a matrix, a tree can then be computed using methods such as neighbor joining. Methods such as ASTRID and NJst (which use this basic approach) are provably statistically consistent, very fast (low degree polynomial time) and have had high accuracy under many conditions that makes them competitive with other popular species tree estimation methods. In this study, inspired by the very recent work of weighted ASTRAL, we present weighted ASTRID, a variant of ASTRID that takes the branch uncertainty on the gene trees into account in the internode distance. Our experimental study evaluating weighted ASTRID shows improvements in accuracy compared to the original (unweighted) ASTRID while remaining fast. Moreover, weighted ASTRID shows competitive accuracy against weighted ASTRAL, the state of the art. Thus, this study provides a new and very fast method for species tree estimation that improves upon ASTRID, has comparable accuracy with the state of the art while remaining much faster. Weighted ASTRID is available at https://github.com/RuneBlaze/internode.

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wQFM: highly accurate genome-scale species tree estimation from weighted quartets

Cited by 19 publications

References 90 publications

Quintet Rooting: rooting species trees under the multi-species coalescent model

Quintet Rooting: rooting species trees under the multi-species coalescent model

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

Fast and Accurate Species Trees from Weighted Internode Distances

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