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Asteroid: a new algorithm to infer species trees from gene trees under high proportions of missing data.
Morel, Benoit; Williams, Tom A; Stamatakis, Alexandros.
Afiliación
  • Morel B; Computational Molecular Evolution Group, Heidelberg Institute for Theoretical Studies, Heidelberg 69118, Germany.
  • Williams TA; Institute for Theoretical Informatics, Karlsruhe Institute of Technology, Karlsruhe 76131, Germany.
  • Stamatakis A; School of Biological Sciences, University of Bristol, Bristol BS8, UK.
Bioinformatics ; 39(1)2023 01 01.
Article en En | MEDLINE | ID: mdl-36576010
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.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Genómica / Especiación Genética Tipo de estudio: Prognostic_studies Idioma: En Revista: Bioinformatics Asunto de la revista: INFORMATICA MEDICA Año: 2023 Tipo del documento: Article País de afiliación: Alemania

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Genómica / Especiación Genética Tipo de estudio: Prognostic_studies Idioma: En Revista: Bioinformatics Asunto de la revista: INFORMATICA MEDICA Año: 2023 Tipo del documento: Article País de afiliación: Alemania
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