ASTRAL-II: coalescent-based species tree estimation with many hundreds of taxa and thousands of genes

By Siavash Mirarab, Tandy J. Warnow. Bioinform., 2015

894 citations recorded by the source corpus.

Motivation: The estimation of species phylogenies requires multiple loci, since different loci can have different trees due to incomplete lineage sorting, modeled by the multi-species coalescent model. We recently developed a coalescent-based method, ASTRAL, which is statistically consistent under the multi-species coalescent model and which is more accurate than other coalescent-based methods on the datasets we examined. ASTRAL runs in polynomial time, by constraining the search space using a set of allowed ‘bipartitions’. Despite the limitation to allowed bipartitions, ASTRAL is statistically consistent. Results: We present a new version of ASTRAL, which we call ASTRAL-II. We show that ASTRAL-II has substantial advantages over ASTRAL: it is faster, can analyze much larger datasets (up to 1000 species and 1000 genes) and has substantially better accuracy under some conditions. ASTRAL’s running time is O(n2k|X|2), and ASTRAL-II’s running time is O(nk|X|2), where n is the number of species, k is the number of loci and X is the set of allowed bipartitions for the search space. Availability and implementation: ASTRAL-II is available in open source at https://github.com/smirarab/ASTRAL and datasets used are available at http://www.cs.utexas.edu/~phylo/datasets/astral2/. Contact: smirarab@gmail.com Supplementary information: Supplementary data are available at Bioinformatics online.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/c3513eccec0461e68a3a33fb660826178e0477a7

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Citations in the corpus

ASTRAL-III: polynomial time species tree reconstruction from partially resolved gene trees (2018, methodology): “To build g , a greedy consensus of all gene trees is computed and is further refined to become binary by applying UPGMA to each polytomy of the greedy tree using a precomputed similarity matrix (see the original paper [22] for details).”
Evaluating Fast Maximum Likelihood-Based Phylogenetic Programs Using Empirical Phylogenomic Data Sets (2017, methodology): “10.12 (Mirarab and Warnow 2015).”
Subgenome dominance in an interspecific hybrid, synthetic allopolyploid, and a 140 year old naturally established neo-allopolyploid monkeyflower (2016, methodology): “2 (Mirarab and Warnow, 2015) with 100 510 bootstrap replicates using the 96 single copy orthogroup trees and their associated bootstrap trees.”
StarBEAST2 Brings Faster Species Tree Inference and Accurate Estimates of Substitution Rates (2016, result): “, 2014), but recent results show that MP-EST should be used with caution as it is sensitive to gene tree errors (Mirarab and Warnow, 2015; Xi et al., 2015).”
Capturing single-copy nuclear genes, organellar genomes, and nuclear ribosomal DNA from deep genome skimming data for plant phylogenetics: A case study in Vitaceae (2021, methodology): “Large datasets of nuclear genes have facilitated the use of species tree methods based on multispecies coalescent models (Mirarab & Warnow, 2015; Edwards et al., 2016), which have greatly increased the accuracy of phylogenetic inference (McCormack et al., 2009; Smith et al., 2015).”
Evolution of lacewings and allied orders using anchored phylogenomics (Neuroptera, Megaloptera, Raphidioptera) (2018, methodology): “9.7 (Mirarab & Warnow, 2015) using bootstrap replicates from the RaxML-estimated gene trees for branch support values estimation. astral tree estimation is used to mitigate the effects of incomplete lineage sorting, and although not necessarily powerful for tree estimation (especially for older…”
STAG: Species Tree Inference from All Genes (2018, methodology): “We used ASTRAL version 5.5.9 with default parameters.”
Ultraconserved elements show utility in phylogenetic inference of Adephaga (Coleoptera) and suggest paraphyly of ‘Hydradephaga’ (2017, methodology): “A bootstrap ≥70% was considered as strong support (Hillis & Bull, 1993) Species tree inference was conducted using astral ii (Mirarab et al., 2014b; Mirarab & Warnow, 2015) and astrid (Vachaspati & Warnow, 2015), following the UCE processing steps of Alexander (2016), as astral and astrid have been…”
TreeShrink: fast and accurate detection of outlier long branches in collections of phylogenetic trees (2018, methodology): “Among other analyses, ASTRALII [35] was used on a collection of gene trees that the authors published, and we use.”
Fast Coalescent-Based Computation of Local Branch Support from Quartet Frequencies (2016, methodology): “For example, ASTRAL (Mirarab and Warnow 2015), a summary method used in many recent studies (e.g., Wickett et al. 2014; Andrade et al. 2015; Giarla and Esselstyn 2015; Grover et al. 2015; Hosner et al. 2015; Laumer et al. 2015; Prum et al. 2015; Streicher et al. 2015), finds the species tree that…”
ClipKIT: A multiple sequence alignment trimming software for accurate phylogenomic inference (2020, methodology): “3 [16], in which single-gene trees were used as input.”