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

Legried, Brandon; Molloy, Erin K.; Warnow, Tandy; Roch, Sébastien

doi:10.1101/821439

Cited by 26 publications

(54 citation statements)

References 55 publications

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“…Legried et al [74] found that ASTRID had similar or higher accuracy than all other methods evaluated. Overall, distance-based methods appear to be a generally accurate and efficient method for inferring species trees using paralogs.…”

Section: Methods Based On Neighbor Joining (And Other Clustering Apprmentioning

confidence: 94%

New approaches for inferring phylogenies in the presence of paralogs

Smith¹,

Hahn²

2020

Preprint

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The availability of whole genome sequences was expected to supply essentially unlimited data for phylogenetics. However, strict reliance on single-copy genes for this purpose has drastically limited the amount of data that can be used. Here, we review several approaches for increasing the amount of data used for phylogenetic inference, focusing on methods that allow for the inclusion of duplicated genes (paralogs). Recently developed methods that are robust to high levels of incomplete lineage sorting also appear to be robust to the inclusion of paralogs, suggesting a promising way to take full advantage of genomic data. We discuss the pitfalls of these approaches, as well as further avenues for research.

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Section: Methods Based On Neighbor Joining (And Other Clustering Apprmentioning

confidence: 94%

New approaches for inferring phylogenies in the presence of paralogs

Smith¹,

Hahn²

2020

Preprint

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“…In fact, ASTRAL-multi is the only method that has been proven statistically consistent under any GDL model. Yet, a comparison reported by Legried et al (2020) between ASTRAL-multi and three earlier species tree estimation methods, including DupTree, STAG ( Emms and Kelly, 2018 ), and MulRF, showed that ASTRAL-multi had good but not exceptional accuracy; specifically, when the duplication and loss rates were both high, ASTRAL-multi was more accurate than DupTree (except when GTEE was low) and STAG (which often failed to complete), but was less accurate than MulRF.…”

Section: Introductionmentioning

confidence: 97%

“…In a very recent advance, Legried et al (2020) proved that ASTRAL-multi ( Rabiee et al , 2019 ), an extension of ASTRAL ( Mirarab et al , 2014 ) to address multi-allele inputs, is statistically consistent under the standard stochastic model of GDL proposed by Arvestad et al (2009) in which all the genes evolve independently and identically distributed ( i.i.d. ) within a species tree, with duplication and loss rates fixed across the edges of the species tree.…”

Section: Introductionmentioning

confidence: 99%

FastMulRFS: fast and accurate species tree estimation under generic gene duplication and loss models

Molloy

Warnow

2020

Bioinformatics

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Motivation Species tree estimation is a basic part of biological research but can be challenging because of gene duplication and loss (GDL), which results in genes that can appear more than once in a given genome. All common approaches in phylogenomic studies either reduce available data or are error-prone, and thus, scalable methods that do not discard data and have high accuracy on large heterogeneous datasets are needed. Results We present FastMulRFS, a polynomial-time method for estimating species trees without knowledge of orthology. We prove that FastMulRFS is statistically consistent under a generic model of GDL when adversarial GDL does not occur. Our extensive simulation study shows that FastMulRFS matches the accuracy of MulRF (which tries to solve the same optimization problem) and has better accuracy than prior methods, including ASTRAL-multi (the only method to date that has been proven statistically consistent under GDL), while being much faster than both methods. Availability and impementation FastMulRFS is available on Github (https://github.com/ekmolloy/fastmulrfs). Supplementary information Supplementary data are available at Bioinformatics online.

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“…Second, we presented FastMulRFS, a polynomial time algorithm to find an exact solution to the RFS-multree problem within a constrained search space, and we proved that the default version is statistically consistent under generic duplication-only or loss-only models. Thus, FastMulRFS is the second of only two methods proven to be statistically consistent under scenarios with gene duplication and/or loss (ASTRAL-multi, which was proven consistent under a parametric model of GDL in [24], is the other). Third, we showed that FastMulRFS maintains high accuracy even under conditions where both duplication and loss occur, where moderate incomplete lineage sorting (ILS) is present, where there is substantial gene tree estimation error (GTEE), and for 25 to 500 genes.…”

Section: Discussionmentioning

confidence: 99%

“…In a recent study, Legried et al [24] showed that ASTRAL-multi [28], a recent extension of ASTRAL [27] (a method that was developed for the problem of species tree estimation in the presence of ILS) is statistically consistent under a standard stochastic model of GDL. They also compared ASTRAL-multi to MulRF and DupTree, and found that it was more accurate than DupTree but not quite as accurate as MulRF, given estimated gene trees where true gene tree heterogeneity was due to GDL.…”

Section: Introductionmentioning

confidence: 99%

FastMulRFS: Fast and accurate species tree estimation under generic gene duplication and loss models

Molloy

Warnow

2019

Preprint

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Species tree estimation is a basic part of much biological research, but can be challenging because of gene duplication and loss (GDL), which results in genes that can appear more than once in a given genome. To construct species trees from gene families, biologists typically pick one copy of each gene in each species (based on estimations of orthology), but orthology detection is not yet reliably accurate, and incorrect orthology inferences can result in incorrect species trees. Restricting datasets to single-copy genes is another common solution, but this reduces the amount of available data, which is undesirable (and even genes that appear to be single-copy may have evolved with duplication and loss). Thus, all common approaches in phylogenomics reduce available data and are error-prone, and methods that do not discard data and have high accuracy on large heterogeneous datasets are needed. Here, we present FastMulRFS, a method for estimating species trees from multrees. We prove that FastMulRFS is polynomial time and statistically consistent under a generic model of gene duplication and loss provided that only duplications occur or only losses occur. We present the results of an extensive simulation study where gene tree heterogeneity is due to gene duplication and loss, incomplete lineage sorting,and gene tree estimation error, and show that FastMulRFS matches the accuracy of MulRF (which tries to solve the same optimization problem) and has better accuracy than ASTRAL-multi (which is statistically consistent under GDL), while being much faster than both methods.

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

Cited by 26 publications

References 55 publications

New approaches for inferring phylogenies in the presence of paralogs

New approaches for inferring phylogenies in the presence of paralogs

FastMulRFS: fast and accurate species tree estimation under generic gene duplication and loss models

FastMulRFS: Fast and accurate species tree estimation under generic gene duplication and loss models

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