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Abstract Advances in phylogenomics and high-throughput sequencing have allowed the reconstruction of deep phylogenetic relationships in the evolution of eukaryotes. Yet, the root of the eukaryotic tree of life remains elusive. The most popular hypothesis in textbooks and reviews is a root between Unikonta (Opisthokonta + Amoebozoa) and Bikonta (all other eukaryotes), which emerged from analyses of a single-gene fusion. Subsequent, highly cited studies based on concatenation of genes supported this hypothesis with some variations or proposed a root within Excavata. However, concatenation of genes does not consider phylogenetically-informative events like gene duplications and losses. A recent study using gene tree parsimony (GTP) suggested the root lies between Opisthokonta and all other eukaryotes, but only including 59 taxa and 20 genes. Here we use GTP with a duplication-loss model in a gene-rich and taxon-rich dataset (i.e., 2,786 gene families from two sets of 155 and 158 diverse eukaryotic lineages) to assess the root, and we iterate each analysis 100 times to quantify tree space uncertainty. We also contrasted our results and discarded alternative hypotheses from the literature using GTP and the likelihood-based method SpeciesRax. Our estimates suggest a root between Fungi or Opisthokonta and all other eukaryotes; but based on further analysis of genome size, we propose that the root between Opisthokonta and all other eukaryotes is the most likely. 

Abstract Estimating multiple sequence alignments (MSAs) and inferring phylogenies are essential for many aspects of comparative biology. Yet, many bioinformatics tools for such analyses have focused on specific clades, with greatest attention paid to plants, animals, and fungi. The rapid increase in high-throughput sequencing (HTS) data from diverse lineages now provides opportunities to estimate evolutionary relationships and gene family evolution across the eukaryotic tree of life. At the same time, these types of data are known to be error-prone (e.g., substitutions, contamination). To address these opportunities and challenges, we have refined a phylogenomic pipeline, now named PhyloToL, to allow easy incorporation of data from HTS studies, to automate production of both MSAs and gene trees, and to identify and remove contaminants. PhyloToL is designed for phylogenomic analyses of diverse lineages across the tree of life (i.e., at scales of >100 My). We demonstrate the power of PhyloToL by assessing stop codon usage in Ciliophora, identifying contamination in a taxon- and gene-rich database and exploring the evolutionary history of chromosomes in the kinetoplastid parasite Trypanosoma brucei, the causative agent of African sleeping sickness. Benchmarking PhyloToL’s homology assessment against that of OrthoMCL and a published paper on superfamilies of bacterial and eukaryotic organellar outer membrane pore-forming proteins demonstrates the power of our approach for determining gene family membership and inferring gene trees. PhyloToL is highly flexible and allows users to easily explore HTS data, test hypotheses about phylogeny and gene family evolution and combine outputs with third-party tools (e.g., PhyloChromoMap, iGTP).