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A simpler linear-time algorithm for the common refinement of rooted phylogenetic trees on a common leaf set.
Schaller, David; Hellmuth, Marc; Stadler, Peter F.
Afiliación
  • Schaller D; Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, Universität Leipzig, Härtelstraße 16-18, 04107, Leipzig, Germany.
  • Hellmuth M; Department of Mathematics, Faculty of Science, Stockholm University, SE-10691, Stockholm, Sweden.
  • Stadler PF; Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, Universität Leipzig, Härtelstraße 16-18, 04107, Leipzig, Germany. studla@bioinf.uni-leipzig.de.
Algorithms Mol Biol ; 16(1): 23, 2021 Dec 06.
Article en En | MEDLINE | ID: mdl-34872590
ABSTRACT

BACKGROUND:

The supertree problem, i.e., the task of finding a common refinement of a set of rooted trees is an important topic in mathematical phylogenetics. The special case of a common leaf set L is known to be solvable in linear time. Existing approaches refine one input tree using information of the others and then test whether the results are isomorphic.

RESULTS:

An O(k|L|) algorithm, LinCR, for constructing the common refinement T of k input trees with a common leaf set L is proposed that explicitly computes the parent function of T in a bottom-up approach.

CONCLUSION:

LinCR is simpler to implement than other asymptotically optimal algorithms for the problem and outperforms the alternatives in empirical comparisons.

AVAILABILITY:

An implementation of LinCR in Python is freely available at https//github.com/david-schaller/tralda .
Palabras clave

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Algorithms Mol Biol Año: 2021 Tipo del documento: Article País de afiliación: Alemania

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Algorithms Mol Biol Año: 2021 Tipo del documento: Article País de afiliación: Alemania