Literature DB >> 28976320

Species Tree Estimation Using ASTRAL: How Many Genes Are Enough?

Shubhanshu Shekhar, Sebastien Roch, Siavash Mirarab.   

Abstract

Species tree reconstruction from genomic data is increasingly performed using methods that account for sources of gene tree discordance such as incomplete lineage sorting. One popular method for reconstructing species trees from unrooted gene tree topologies is ASTRAL. In this paper, we derive theoretical sample complexity results for the number of genes required by ASTRAL to guarantee reconstruction of the correct species tree with high probability. We also validate those theoretical bounds in a simulation study. Our results indicate that ASTRAL requires gene trees to reconstruct the species tree correctly with high probability where is the number of species and is the length of the shortest branch in the species tree. Our simulations, some under the anomaly zone, show trends consistent with the theoretical bounds and also provide some practical insights on the conditions where ASTRAL works well.

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Year:  2017        PMID: 28976320     DOI: 10.1109/TCBB.2017.2757930

Source DB:  PubMed          Journal:  IEEE/ACM Trans Comput Biol Bioinform        ISSN: 1545-5963            Impact factor:   3.710


  4 in total

1.  Testing for Polytomies in Phylogenetic Species Trees Using Quartet Frequencies.

Authors:  Erfan Sayyari; Siavash Mirarab
Journal:  Genes (Basel)       Date:  2018-02-28       Impact factor: 4.096

2.  ASTRAL-Pro: Quartet-Based Species-Tree Inference despite Paralogy.

Authors:  Chao Zhang; Celine Scornavacca; Erin K Molloy; Siavash Mirarab
Journal:  Mol Biol Evol       Date:  2020-11-01       Impact factor: 16.240

3.  The large-sample asymptotic behaviour of quartet-based summary methods for species tree inference.

Authors:  Yao-Ban Chan; Qiuyi Li; Celine Scornavacca
Journal:  J Math Biol       Date:  2022-08-17       Impact factor: 2.164

4.  ASTRAL-III: polynomial time species tree reconstruction from partially resolved gene trees.

Authors:  Chao Zhang; Maryam Rabiee; Erfan Sayyari; Siavash Mirarab
Journal:  BMC Bioinformatics       Date:  2018-05-08       Impact factor: 3.169

  4 in total

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