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Does the choice of nucleotide substitution models matter topologically?

Hoff, Michael; Orf, Stefan; Riehm, Benedikt; Darriba, Diego; Stamatakis, Alexandros

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 ... mehr

Zugehörige Institution(en) am KIT Fakultät für Informatik (INFORMATIK)
Publikationstyp Zeitschriftenaufsatz
Jahr 2016
Sprache Englisch
Identifikator DOI: 10.1186/s12859-016-0985-x
ISSN: 1471-2105
URN: urn:nbn:de:swb:90-627281
KITopen ID: 1000062728
Erschienen in BMC bioinformatics
Band 17
Heft 1
Seiten Art.Nr.: 143
Lizenz CC BY 4.0: Creative Commons Namensnennung 4.0 International
Schlagworte Phylogenetics, Nucleotide substitution, Model selection, Information criterion, BIC, AIC
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