The statistical selection of best-fit models of nucleotide substitution is routine in the phylogenetic analysis of DNA sequence alignments. The programs ModelTest1 and jModelTest2 are very popular tools to accomplish this task, with thousands of users and citations. The latter uses PhyML3 to obtain maximum likelihood estimates of model parameters, and implements different statistical criteria to select among 88 models of nucleotide substitution, including hierarchical and dynamical likelihood ratio tests, Akaike’s and Bayesian information criteria (AIC and BIC) and a performance-based decision theory method (see ref. 4). jModelTest also provides estimates of model selection uncertainty, parameter importances and model-averaged parameter estimates, including model-averaged phylogenies4.

However, in recent years the advent of NGS technologies has changed the field, and most researchers are now moving from phylogenetics to phylogenomics, where large sequence alignments typically include hundreds or thousands of loci. Phylogenetic resources therefore need to be adapted to a High Performance Computing (HPC) paradigm, allowing demanding analyses at the genomic level. Here we introduce jModelTest 2, which incorporates more models, new heuristics, efficient technical optimizations and multithreaded and MPI-based implementations for statistical model selection.