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ModelTest-NG: A New and Scalable Tool for the Selection of DNA and Protein Evolutionary Models.
Darriba, Diego; Posada, David; Kozlov, Alexey M; Stamatakis, Alexandros; Morel, Benoit; Flouri, Tomas.
Afiliación
  • Darriba D; Computer Architecture Group, Centro de investigación CITIC, Universidade da Coruña, Elviña, A Coruña, Spain.
  • Posada D; Computational Molecular Evolution Group, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany.
  • Kozlov AM; Department of Biochemistry, Genetics, and Immunology, University of Vigo, Vigo, Spain.
  • Stamatakis A; Biomedical Research Center (CINBIO), University of Vigo, Vigo, Spain.
  • Morel B; Galicia Sur Health Research Institute, Vigo, Spain.
  • Flouri T; Computational Molecular Evolution Group, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany.
Mol Biol Evol ; 37(1): 291-294, 2020 Jan 01.
Article en En | MEDLINE | ID: mdl-31432070
ModelTest-NG is a reimplementation from scratch of jModelTest and ProtTest, two popular tools for selecting the best-fit nucleotide and amino acid substitution models, respectively. ModelTest-NG is one to two orders of magnitude faster than jModelTest and ProtTest but equally accurate and introduces several new features, such as ascertainment bias correction, mixture, and free-rate models, or the automatic processing of single partitions. ModelTest-NG is available under a GNU GPL3 license at https://github.com/ddarriba/modeltest , last accessed September 2, 2019.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Técnicas Genéticas / Evolución Molecular / Sustitución de Aminoácidos / Modelos Genéticos Tipo de estudio: Evaluation_studies Idioma: En Revista: Mol Biol Evol Asunto de la revista: BIOLOGIA MOLECULAR Año: 2020 Tipo del documento: Article País de afiliación: España

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Técnicas Genéticas / Evolución Molecular / Sustitución de Aminoácidos / Modelos Genéticos Tipo de estudio: Evaluation_studies Idioma: En Revista: Mol Biol Evol Asunto de la revista: BIOLOGIA MOLECULAR Año: 2020 Tipo del documento: Article País de afiliación: España