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Asteroid: a new algorithm to infer species trees from gene trees under high proportions of missing data

Morel, Benoit 1; Williams, Tom A.; Stamatakis, Alexandros ORCID iD icon 1
1 Institut für Theoretische Informatik (ITI), Karlsruher Institut für Technologie (KIT)

Abstract:

Motivation: Missing data and incomplete lineage sorting (ILS) are two major obstacles to accurate species tree inference. Gene tree summary methods such as ASTRAL and ASTRID have been developed to account for ILS. However, they can be severely affected by high levels of missing data.
Results: We present Asteroid, a novel algorithm that infers an unrooted species tree from a set of unrooted gene trees. We show on both empirical and simulated datasets that Asteroid is substantially more accurate than ASTRAL and ASTRID for very high proportions (>80%) of missing data. Asteroid is several orders of magnitude faster than ASTRAL for datasets that contain thousands of genes. It offers advanced features such as parallelization, support value computation and support for multi-copy and multifurcating gene trees.
Availability and implementation: Asteroid is freely available at https://github.com/BenoitMorel/Asteroid.
Supplementary information: Supplementary data are available at Bioinformatics online.


Verlagsausgabe §
DOI: 10.5445/IR/1000155464
Veröffentlicht am 03.02.2023
Originalveröffentlichung
DOI: 10.1093/bioinformatics/btac832
Scopus
Zitationen: 7
Web of Science
Zitationen: 4
Dimensions
Zitationen: 12
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Theoretische Informatik (ITI)
Publikationstyp Zeitschriftenaufsatz
Publikationsdatum 01.01.2023
Sprache Englisch
Identifikator ISSN: 1367-4811, 0266-7061, 1367-4803, 1460-2059
KITopen-ID: 1000155464
Erschienen in Bioinformatics (Oxford, England)
Verlag Oxford University Press (OUP)
Band 39
Heft 1
Seiten Art.-Nr.: btac832
Vorab online veröffentlicht am 28.12.2022
Nachgewiesen in Web of Science
Dimensions
Scopus
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