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dc.contributor.authorOrtuño Guzmán, Francisco Manuel
dc.contributor.authorValenzuela Cansino, Olga 
dc.contributor.authorRojas Ruiz, Fernando José 
dc.contributor.authorPomares Cintas, Héctor Emilio 
dc.contributor.authorPérez Florido, Javier
dc.contributor.authorUrquiza Ortiz, José Miguel
dc.contributor.authorRojas Ruiz, Ignacio 
dc.date.accessioned2025-01-29T11:16:46Z
dc.date.available2025-01-29T11:16:46Z
dc.date.issued2013-06-21
dc.identifier.urihttps://hdl.handle.net/10481/100937
dc.description.abstractMotivation: Multiple sequence alignments (MSAs) are widely used approaches in bioinformatics to carry out other tasks such as structure predictions, biological function analyses or phylogenetic modeling. However, current tools usually provide partially optimal alignments, as each one is focused on specific biological features. Thus, the same set of sequences can produce different alignments, above all when sequences are less similar. Consequently, researchers and biologists do not agree about which is the most suitable way to evaluate MSAs. Recent evaluations tend to use more complex scores including further biological features. Among them, 3D structures are increasingly being used to evaluate alignments. Because structures are more conserved in proteins than sequences, scores with structural information are better suited to evaluate more distant relationships between sequences. Results: The proposed multiobjective algorithm, based on the non-dominated sorting genetic algorithm, aims to jointly optimize three objectives: STRIKE score, non-gaps percentage and totally conserved columns. It was significantly assessed on the BAliBASE benchmark according to the Kruskal–Wallis test (P < 0.01). This algorithm also outperforms other aligners, such as ClustalW, Multiple Sequence Alignment Genetic Algorithm (MSA-GA), PRRP, DIALIGN, Hidden Markov Model Training (HMMT), Pattern-Induced Multi-sequence Alignment (PIMA), MULTIALIGN, Sequence Alignment Genetic Algorithm (SAGA), PILEUP, Rubber Band Technique Genetic Algorithm (RBT-GA) and Vertical Decomposition Genetic Algorithm (VDGA), according to the Wilcoxon signed-rank test (P < 0.05), whereas it shows results not significantly different to 3D-COFFEE (P > 0.05) with the advantage of being able to use less structures. Structural information is included within the objective function to evaluate more accurately the obtained alignments.es_ES
dc.description.sponsorshipSpanish CICYT Project [SAF2010-20558 (in part)] and the government of Andalusia Project [P09-TIC-175476].es_ES
dc.language.isoenges_ES
dc.publisherOxford Academices_ES
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivs 3.0 Licensees_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es_ES
dc.titleOptimizing multiple sequence alignments using a genetic algorithm based on three objectives: structural information, non-gaps percentage and totally conserved columnses_ES
dc.typejournal articlees_ES
dc.rights.accessRightsopen accesses_ES
dc.identifier.doi10.1093/bioinformatics/btt360
dc.type.hasVersionAMes_ES


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