Scalable Empirical Mixture Models That Account for Across-Site Compositional Heterogeneity.

ABSTRACT: Biochemical demands constrain the range of amino acids acceptable at specific sites resulting in across-site compositional heterogeneity of the amino acid replacement process. Phylogenetic models that disregard this heterogeneity are prone to systematic errors, which can lead to severe long-branch attraction artifacts. State-of-the-art models accounting for across-site compositional heterogeneity include the CAT model, which is computationally expensive, and empirical distribution mixture models estimated via maximum likelihood (C10-C60 models). Here, we present a new, scalable method EDCluster for finding empirical distribution mixture models involving a simple cluster analysis. The cluster analysis utilizes specific coordinate transformations which allow the detection of specialized amin

SUBMITTER: Schrempf D 

PROVIDER: S-EPMC7743758 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature