Statistical binning enables an accurate coalescent-based estimation of the avian tree

Mirarab, Siavash; Bayzid, Md. Shamsuzzoha; Boussau, Bastien; Warnow, Tandy

doi:10.1126/science.1250463

Cited by 257Genomics and Phylogenetic StudiesGenetic diversity and population structureEvolution and Paleontology Studies

Statistical binning enables an accurate coalescent-based estimation of the avian tree

Mirarab, Siavash, Bayzid, Md. Shamsuzzoha, Boussau, Bastien, Warnow, Tandy·2014·Science

DOI 10.1126/science.1250463

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Abstract

Gene tree incongruence arising from incomplete lineage sorting (ILS) can reduce the accuracy of concatenation-based estimations of species trees. Although coalescent-based species tree estimation methods can have good accuracy in the presence of ILS, they are sensitive to gene tree estimation error. We propose a pipeline that uses bootstrapping to evaluate whether two genes are likely to have the same tree, then it groups genes into sets using a graph-theoretic optimization and estimates a tree on each subset using concatenation, and finally produces an estimated species tree from these trees using the preferred coalescent-based method. Statistical binning improves the accuracy of MP-EST, a popular coalescent-based method, and we use it to produce the first genome-scale coalescent-based avian tree of life.

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JarvD5b was derived from concatenating single-gene alignments in JarvD5a into a smaller number of longer partitions, resulting in enhanced phylogenetic signal measured by average bootstrap support (ABS) of gene tree.
“JarvD5b was derived from concatenating single-gene alignments in JarvD5a into a smaller number of longer partitions (Mirarab et al. 2014), resulting in enhanced phylogenetic signal (measured by average bootstrap support, or ABS, of gene tree)”
From Evaluating fast maximum likelihood-based phylogenetic programs using empirical phylogenomic data sets (2018) · cites this paper as Mirarab et al. 2014

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ASTRAL: genome-scale coalescent-based species tree estimation
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RESEARCH ARTICLE Weighted Statistical Binning: Enabling Statistically Consistent Genome-Scale Phylogenetic Analyses
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DOI:10.1093/sysbio/syu063 Evaluating Summary Methods for Multilocus Species Tree Estimation in the Presence of Incomplete Lineage Sorting
Mirarab, S., Bayzid, M. S., & Warnow, T. (2016)
Self-citation

Authors: Mirarab, S., Bayzid, M. S., Boussau, B., & Warnow, T.
Affiliations: The University of Texas at Austin (US)
Lyon 1 Université (FR)
University of Illinois Urbana-Champaign (US)
Corresponding author: Warnow, Tandy
ORCID: Mirarab, Siavash
Bayzid, Md. Shamsuzzoha
Boussau, Bastien
Warnow, Tandy
Venue: Science
Publisher: American Association for the Advancement of Science (AAAS)
Published: 2014-12-12
Volume: 346
Issue: 6215
Pages: 1250463-1250463
ISSN: 0036-8075 (ISSN-L)
1095-9203
DOI: 10.1126/science.1250463
OpenAlex: W2083015817
Cited by: 257
References: 72
Citations by year: 2026: 11 · 2025: 11 · 2024: 10 · 2023: 14 · 2022: 21 · 2021: 19 · 2020: 28 · 2019: 34 · 2018: 25 · 2017: 33 · 2016: 28 · 2015: 22 · 2014: 1
FWCI: 11.42
Type: journal article
Language: en
Open access: closed
Locations: Science (publishedVersion)
PubMed (publishedVersion)
HAL (submittedVersion)
Funders: National Science Foundation
Howard Hughes Medical Institute
University of Alberta
Agence Nationale de la Recherche (ANR-10-BINF-0001)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic diversity and population structure
Physical Sciences › Earth and Planetary Sciences › Paleontology › Evolution and Paleontology Studies
Keywords: species tree estimation, coalescent methods, incomplete lineage sorting, gene tree discordance, bootstrapping, graph optimization, maximum likelihood estimation, avian phylogeny, genome-scale phylogeny
MeSH terms: Animals, Birds, Mammals, Phylogeny, Vertebrates, Yeasts, Likelihood Functions, Sequence Alignment, Genome, Computational Biology

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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Statistical binning enables an accurate coalescent-based estimation of the avian tree

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