Efficiently sparse listing of classes of optimal cophylogeny reconciliations

Yishu Wang (1, 2) , Marie-France Sagot (2, 1) , Arnaud Mary (2, 1) , Blerina Sinaimeri (2, 1, 3)

1 ERABLE - Equipe de recherche européenne en algorithmique et biologie formelle et expérimentale
2 Baobab
3 LUISS - Libera Università Internazionale degli Studi Sociali Guido Carli [Roma]

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.

Domaines: Bio-informatique [q-bio.QM]; Systématique, phylogénie et taxonomie

Yishu Wang, Marie-France Sagot, Arnaud Mary, Blerina Sinaimeri. Efficiently sparse listing of classes of optimal cophylogeny reconciliations. Algorithms for Molecular Biology, 2022, 17 (1), pp.1-16. ⟨10.1186/s13015-022-00206-y⟩. ⟨hal-03576371⟩