NJst and ASTRID are not statistically consistent under a random model of missing data

Rhodes, John A.; Nute, Michael G.; Warnow, Tandy

doi:10.48550/arxiv.2001.07844

Cited by 3 publications

(5 citation statements)

References 9 publications

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“…Under the i.i.d. model, ASTRAL is statistically consistent while ASTRID is not [9,47], but both methods are statistically consistent under the clade-based model. That said, the condition under which ASTRAL is consistent for the clade-based model requires that it be run in exact mode, rather than the heuristic mode where a set of allowed bipartitions is computed and the returned tree must draw its bipartitions from that set [47].…”

Section: Impact Of Missing Datamentioning

confidence: 82%

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Weighted ASTRID: fast and accurate species trees from weighted internode distances

Liu

Warnow

2023

Algorithms Mol Biol

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Background 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. Results Our experimental study evaluating weighted ASTRID typically shows improvements in accuracy compared to the original (unweighted) ASTRID, and shows competitive accuracy against weighted ASTRAL, the state of the art. Our re-implementation of ASTRID also improves the runtime, with marked improvements on large datasets. Conclusions Weighted ASTRID is a new and very fast method for species tree estimation that typically improves upon ASTRID and has comparable accuracy to weighted ASTRAL, while remaining much faster. Weighted ASTRID is available at https://github.com/RuneBlaze/internode.

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Section: Impact Of Missing Datamentioning

confidence: 82%

“…In recent years, many summary methods that are statistically consistent under the MSC have been developed, such as MP-EST [8], NJst [9], ASTRAL [6], ASTRID [10], FASTRAL [11], and wQFM [12]. Many of these methods are scalable to thousands of species with genomic-scale data (i.e., with thousands of genes).…”

Section: Introductionmentioning

confidence: 99%

Weighted ASTRID: fast and accurate species trees from weighted internode distances

Liu

Warnow

2023

Algorithms Mol Biol

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“…Nonetheless, it is impossible to know how many gene trees are sufficient to alleviate the effects of missing data. In addition, it has been shown that, under specific conditions and for some models of taxon deletion, ASTRID is guaranteed to converge to an incorrect species tree when the number of gene trees increases asymptotically (Rhodes et al ., 2020).…”

Section: Discussionmentioning

confidence: 99%

“…We refer to the models describing this stochastic process as models of taxon deletion . It has been shown that under some very specific models of taxon deletion, ASTRAL-I and ASTRAL-II are still consistent under the MSC model (Nute and Chou, 2017), but that ASTRID is inconsistent (Rhodes et al ., 2020). However, the consistency of ASTRAL as well as other methods such as FastRFS has not yet been established for more general models of taxon deletion.…”

Section: Introductionmentioning

confidence: 99%

Asteroid: a new minimum balanced evolution supertree algorithm robust to missing data

Morel

Williams

Stamatakis

2022

Preprint

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Motivation: Missing data and incomplete lineage sorting are two major obstacles to accurate species tree inference. Gene tree methods such as ASTRAL and ASTRID have been developed to account for incomplete lineage sorting. However, they can be severely affected by high levels of missing data. Results: We present Asteroid, a novel supertree method that infers an unrooted species tree from a set of unrooted gene trees. We show on both empirical and simulated datasets that Asteroid is more robust to missing data than ASTRAL and ASTRID, while being several orders of magnitude faster than ASTRAL for datasets that contain thousands of genes. Asteroid offers advanced features such as parallelization, support value computation, and support for multi-copy and multifurcating gene trees. Availability: Asteroid is freely available at \https://github.com/BenoitMorel/Asteroid

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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

Self Cite

View full text Add to dashboard Cite

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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NJst and ASTRID are not statistically consistent under a random model of missing data

Cited by 3 publications

References 9 publications

Weighted ASTRID: fast and accurate species trees from weighted internode distances

Weighted ASTRID: fast and accurate species trees from weighted internode distances

Asteroid: a new minimum balanced evolution supertree algorithm robust to missing data

Fast and Accurate Species Trees from Weighted Internode Distances

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