The effects of model choice and mitigating bias on the ribosomal tree of life

Deep-level relationships within Bacteria, Archaea, and Eukarya as well as the relationships of these three domains to each other require resolution. The ribosomal machinery, universal to all cellular life, represents a protein repertoire resistant to horizontal gene transfer, which provides a largely congruent signal necessary for reconstructing a tree suitable as a backbone for life's reticulate history. Here, we generate a ribosomal tree of life from a robust taxonomic sampling of Bacteria, Archaea, and Eukarya to elucidate deep-level intra-domain and inter-domain relationships. Lack of phylogenetic information and systematic errors caused by inadequate models (that cannot account for substitution rate or compositional heterogeneities) or improper model selection compound conflicting phylogenetic signals from HGT and/or paralogy. Thus, we tested several models of varying sophistication on three different datasets, performed removal of fast-evolving or long-branched Archaea and Eukarya, and employed three different strategies to remove compositional heterogeneity to examine their effects on the topological outcome. Our results support a two-domain topology for the tree of life, where Eukarya emerges from within Archaea as sister to a Korarchaeota/Thaumarchaeota (KT) or Crenarchaeota/KT clade for all models under all or at least one of the strategies employed. Taxonomic manipulation allows single-matrix and certain mixture models to vacillate between two-domain and three-domain phylogenies. We find that models vary in their ability to resolve different areas of the tree of life, which does not necessarily correlate with model complexity. For example, both single-matrix and some mixture models recover monophyletic Crenarchaeota and Euryarchaeota archaeal phyla. In contrast, the most sophisticated model recovers a paraphyletic Euryarchaeota but detects two large clades that comprise the Bacteria, which were recovered separately but never together in the other models. Overall, models recovered consistent topologies despite dataset modifications due to the removal of compositional bias, which reflects either ineffective bias reduction or robust datasets that allow models to overcome reconstruction artifacts. We recommend a comparative approach for evolutionary models to identify model weaknesses as well as consensus relationships.