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Efficiently sparse listing of classes of optimal cophylogeny reconciliations.
Wang, Yishu; Mary, Arnaud; Sagot, Marie-France; Sinaimeri, Blerina.
Afiliación
  • Wang Y; Université de Lyon, Université Lyon 1, CNRS, Laboratoire de Biométrie et Biologie Evolutive UMR 5558, 69622, Villeurbanne, France.
  • Mary A; ERABLE team, Inria Grenoble Rhône-Alpes, Villeurbanne, France.
  • Sagot MF; Université de Lyon, Université Lyon 1, CNRS, Laboratoire de Biométrie et Biologie Evolutive UMR 5558, 69622, Villeurbanne, France.
  • Sinaimeri B; ERABLE team, Inria Grenoble Rhône-Alpes, Villeurbanne, France.
Algorithms Mol Biol ; 17(1): 2, 2022 Feb 15.
Article en En | MEDLINE | ID: mdl-35168648
BACKGROUND: Cophylogeny reconciliation is a powerful method for analyzing host-parasite (or host-symbiont) co-evolution. It models co-evolution as an optimization problem where the set of all optimal solutions may represent different biological scenarios which thus need to be analyzed separately. Despite the significant research done in the area, few approaches have addressed the problem of helping the biologist deal with the often huge space of optimal solutions. RESULTS: In this paper, we propose a new approach to tackle this problem. We introduce three different criteria under which two solutions may be considered biologically equivalent, and then we propose polynomial-delay algorithms that enumerate only one representative per equivalence class (without listing all the solutions). CONCLUSIONS: Our results are of both theoretical and practical importance. Indeed, as shown by the experiments, we are able to significantly reduce the space of optimal solutions while still maintaining important biological information about the whole space.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Algorithms Mol Biol Año: 2022 Tipo del documento: Article País de afiliación: Francia Pais de publicación: Reino Unido

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Algorithms Mol Biol Año: 2022 Tipo del documento: Article País de afiliación: Francia Pais de publicación: Reino Unido