Automatic selection of partitioning schemes for phylogenetic analyses using iterative k-means clustering of site rates


Model selection is a vital part of most phylogenetic analyses, and accounting for the heterogeneity in evolutionary patterns across sites is particularly important. Mixture models and partitioning are commonly used to account for this variation, and partitioning is the most popular approach. Most current partitioning methods require some a priori partitioning scheme to be defined, typically guided by known structural features of the sequences, such as gene boundaries or codon positions. Recent evidence suggests that these a priori boundaries often fail to adequately account for variation in rates and patterns of evolution among sites.

Furthermore, new phylogenomic datasets such as those assembled from ultra-conserved elements lack obvious structural features on which to define a priori partitioning schemes. The upshot is that, for many phylogenetic datasets, partitioned models of molecular evolution may be inadequate, thus limiting the accuracy of downstream phylogenetic analyses.