The impact of software and criteria on the selection of best-fit nucleotide substitution models for molecular evolutionary genetic analysis.

Abstract

The statistical selection of best-fit models of nucleotide substitution for multiple sequence alignments (MSAs) is routine in phylogenetics. Our analysis of model selection across three widely used phylogenetic programs (jModelTest2, ModelTest-NG, and IQ-TREE) demonstrated that the choice of program did not significantly affect the ability to accurately identify the true nucleotide substitution model. This finding indicates that researchers can confidently rely on any of these programs for model selection, as they offer comparable accuracy without substantial differences. However, our results underscore the critical impact of the information criterion chosen for model selection. BIC consistently outperformed both AIC and AICc in accurately identifying the true model, regardless of the program used. This observation highlights the importance of carefully selecting the information criterion, with a preference for BIC, when determining the best-fit model for phylogenetic analyses. This study provides an assessment of popular model selection programs while contributing to the advancement of more robust statistical methods and tools for accurately identifying the most suitable nucleotide substitution models.