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dc.contributor.authorHoff, Michael
dc.contributor.authorOrf, Stefan
dc.contributor.authorRiehm, Benedikt
dc.contributor.authorDarriba, Diego
dc.contributor.authorStamatakis, Alexandros
dc.date.accessioned2018-08-29T08:35:33Z
dc.date.available2018-08-29T08:35:33Z
dc.date.issued2016-03-24
dc.identifier.citationHoff, M., Orf, S., Riehm, B., Darriba, D., & Stamatakis, A. (2016). Does the choice of nucleotide substitution models matter topologically?. BMC bioinformatics, 17(1), 143.es_ES
dc.identifier.issn1471-2105
dc.identifier.urihttp://hdl.handle.net/2183/20981
dc.description.abstract[Abstract] Background: In the context of a master level programming practical at the computer science department of the Karlsruhe Institute of Technology, we developed and make available an open-source code for testing all 203 possible nucleotide substitution models in the Maximum Likelihood (ML) setting under the common Akaike, corrected Akaike, and Bayesian information criteria. We address the question if model selection matters topologically, that is, if conducting ML inferences under the optimal, instead of a standard General Time Reversible model, yields different tree topologies. We also assess, to which degree models selected and trees inferred under the three standard criteria (AIC, AICc, BIC) differ. Finally, we assess if the definition of the sample size (#sites versus #sites × #taxa) yields different models and, as a consequence, different tree topologies. Results: We find that, all three factors (by order of impact: nucleotide model selection, information criterion used, sample size definition) can yield topologically substantially different final tree topologies (topological difference exceeding 10 %) for approximately 5 % of the tree inferences conducted on the 39 empirical datasets used in our study. Conclusions: We find that, using the best-fit nucleotide substitution model may change the final ML tree topology compared to an inference under a default GTR model. The effect is less pronounced when comparing distinct information criteria. Nonetheless, in some cases we did obtain substantial topological differences.es_ES
dc.language.isoenges_ES
dc.publisherBioMed Central Ltd.es_ES
dc.relation.urihttps://doi.org/10.1186/s12859-016-0985-xes_ES
dc.rightsAtribución 3.0 Españaes_ES
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectPhylogeneticses_ES
dc.subjectNucleotide substitutiones_ES
dc.subjectModel selectiones_ES
dc.subjectInformation criteriones_ES
dc.subjectBICes_ES
dc.subjectAICes_ES
dc.titleDoes the choice of nucleotide substitution models matter topologically?es_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.journalTitleBMC Bioinformaticses_ES
UDC.volume17es_ES
UDC.issue1es_ES
UDC.startPage143es_ES
dc.identifier.doi10.1186/s12859-016-0985-x


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