Keeling, P. J. & Burki, F. Progress towards the tree of eukaryotes. Curr. Biol. 29, R808–R817 (2019).
Google Scholar
Gawryluk, R. M. R. et al. Non-photosynthetic predators are sister to red algae. Nature 572, 240–243 (2019).
Google Scholar
Janouškovec, J. et al. A new lineage of eukaryotes illuminates early mitochondrial genome reduction. Curr. Biol. 27, 3717–3724 (2017).
Google Scholar
Lax, G. et al. Hemimastigophora is a novel supra-kingdom-level lineage of eukaryotes. Nature 564, 410–414 (2018).
Google Scholar
Oren, A. Prokaryote diversity and taxonomy: current status and future challenges. Philos. Trans. R. Soc. Lond. B 359, 623–638 (2004).
Google Scholar
Shu, W. S. & Huang, L. N. Microbial diversity in extreme environments. Nat. Rev. Microbiol. 20, 219–235 (2022).
Google Scholar
Massana, R., del Campo, J., Sieracki, M. E., Audic, S. & Logares, R. Exploring the uncultured microeukaryote majority in the oceans: reevaluation of ribogroups within stramenopiles. ISME J. 8, 854–866 (2014).
Google Scholar
de Vargas, C. et al. Eukaryotic plankton diversity in the sunlit ocean. Science 348, 1261605 (2015).
Google Scholar
Flegontova, O. et al. Extreme diversity of diplonemid eukaryotes in the ocean. Curr. Biol. 26, 3060–3065 (2016).
Google Scholar
Ahlering, M. A. & Carrel, J. E. Predators are rare even when they are small. Oikos 95, 471–475 (2001).
Google Scholar
Hehenberger, E. et al. Novel predators reshape holozoan phylogeny and reveal the presence of a two-component signaling system in the ancestor of animals. Curr. Biol. 27, 2043–2050 (2017).
Google Scholar
Tikhonenkov, D. V. et al. Description of Colponema vietnamica sp. n. and Acavomonas peruviana n. gen. n. sp., two new alveolate phyla (Colponemidia nom. nov. and Acavomonidia nom. nov.) and their contributions to reconstructing the ancestral state of alveolates and eukaryotes. PLoS ONE 9, e95467 (2014).
Google Scholar
Tikhonenkov, D. V. et al. New lineage of microbial predators adds complexity to reconstructing the evolutionary origin of animals. Curr. Biol. 30, 4500–4509 (2020).
Google Scholar
Mylnikov, A. P. & Tikhonenkov, D. V. The new alveolate carnivorous flagellate Colponema marisrubri sp. n. (Colponemida, Alveolata) from the Red Sea. Zool. Zh. 88, 1163–1169 (2009).
Strassert, J. F. H., Irisarri, I., Williams, T. A. & Burki, F. A molecular timescale for eukaryote evolution with implications for the origin of red algal-derived plastids. Nat. Commun. 12, 1879 (2021).
Google Scholar
Rodriguez-Ezpeleta, N. et al. Detecting and overcoming systematic errors in genome-scale phylogenies. Syst. Biol. 56, 389–399 (2007).
Google Scholar
Strassert, J. F. H., Jamy, M., Mylnikov, A. P., Tikhonenkov, D. V. & Burki, F. New phylogenomic analysis of the enigmatic phylum Telonemia further resolves the eukaryote tree of life. Mol. Biol. Evol. 36, 757–765 (2019).
Google Scholar
Lanfear, R., Kokko, H. & Eyre-Walker, A. Population size and the rate of evolution. Trends Ecol. Evol. 29, 33–41 (2014).
Google Scholar
Bahler, M. & Rhoads, A. Calmodulin signaling via the IQ motif. FEBS Lett. 513, 107–113 (2002).
Google Scholar
Schaffer, D. E., Iyer, L. M., Burroughs, A. M. & Aravind, L. Functional innovation in the evolution of the calcium-dependent system of the eukaryotic endoplasmic reticulum. Front. Genet. 11, 34 (2020).
Google Scholar
Morita-Yamamuro, C. et al. The Arabidopsis gene CAD1 controls programmed cell death in the plant immune system and encodes a protein containing a MACPF domain. Plant Cell Physiol. 46, 902–912 (2005).
Google Scholar
Rosado, C. J. et al. The MACPF/CDC family of pore-forming toxins. Cell. Microbiol. 10, 1765–1774 (2008).
Google Scholar
Ishino, T., Chinzei, Y. & Yuda, M. A Plasmodium sporozoite protein with a membrane attack complex domain is required for breaching the liver sinusoidal cell layer prior to hepatocyte infection. Cell. Microbiol. 7, 199–208 (2005).
Google Scholar
Satoh, H., Oshiro, N., Iwanaga, S., Namikoshi, M. & Nagai, H. Characterization of PsTX-60B, a new membrane-attack complex/perforin (MACPF) family toxin, from the venomous sea anemone Phyllodiscus semoni. Toxicon 49, 1208–1210 (2007).
Google Scholar
Tikhonenkov, D. V., Mazei, Y. A. & Embulaeva, E. A. Degradation succession of heterotrophic flagellate communities in microcosms. Zh. Obs. Biol. 69, 57–64 (2008).
Google Scholar
Tikhonenkov, D. V. et al. On the origin of TSAR: morphology, diversity and phylogeny of Telonemia. Open Biol. 12, 210325 (2022).
Google Scholar
Picelli, S. et al. Full-length RNA-seq from single cells using Smart-seq2. Nat. Protoc. 9, 171–181 (2014).
Google Scholar
Keeling, P. J., Poulson, N. & McFadden, G. I. Phylogenetic diversity of parabasalian symbionts from termites, including the phylogenetic position of Pseudotrypanosoma and Trichonympha. J. Eukaryot. Microbiol. 45, 643–650 (1998).
Google Scholar
Medlin, L., Elwood, H. J., Stickel, S. & Sogin, M. L. The characterization of enzymatically amplified eukaryotic 16S-like rRNA-coding regions. Gene 71, 491–499 (1988).
Google Scholar
Tikhonenkov, D. V., Janouškovec, J., Keeling, P. J. & Mylnikov, A. P. The morphology, ultrastructure and SSU rRNA gene sequence of a new freshwater flagellate, Neobodo borokensis n. sp. (Kinetoplastea, Excavata). J. Eukaryot. Microbiol. 63, 220–232 (2016).
Google Scholar
Andrews, S. FastQC: a quality control tool for high throughput sequence data (Babraham Bioinformatics, 2010); https://www.bioinformatics.babraham.ac.uk/projects/fastqc/.
Zhang, J., Kobert, K., Flouri, T. & Stamatakis, A. PEAR: a fast and accurate Illumina Paired-End reAd mergeR. Bioinformatics 30, 614–620 (2013).
Google Scholar
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).
Google Scholar
Grabherr, M. G. et al. Full-length transcriptome assembly from RNA-seq data without a reference genome. Nat. Biotechnol. 29, 644–652 (2011).
Google Scholar
Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).
Google Scholar
Laetsch, D. R. & Blaxter, M. L. BlobTools: interrogation of genome assemblies. F1000Research 6, 1287 (2017).
Google Scholar
Haas, B. J. et al. Denovo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis. Nat. Protoc. 8, 1494–1512 (2013).
Google Scholar
Li, W. & Godzik, A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22, 1658–1659 (2006).
Google Scholar
Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015).
Google Scholar
Shen, W. & Ren, H. TaxonKit: a practical and efficient NCBI taxonomy toolkit. J. Genet. Genomics 48, 844–850 (2021).
Richter, D. J. et al. EukProt: a database of genome-scale predicted proteins across the diversity of eukaryotes. Peer Community Journal 2, e56 (2022).
Simao, F. A., Waterhouse, R. M., Ioannidis, P., Kriventseva, E. V. & Zdobnov, E. M. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics 31, 3210–3212 (2015).
Google Scholar
Kanehisa, M., Furumichi, M., Sato, Y., Ishiguro-Watanabe, M. & Tanabe, M. KEGG: integrating viruses and cellular organisms. Nucleic Acids Res. 49, D545–D551 (2021).
Google Scholar
Moriya, Y., Itoh, M., Okuda, S., Yoshizawa, A. C. & Kanehisa, M. KAAS: an automatic genome annotation and pathway reconstruction server. Nucleic Acids Res. 35, W182–W185 (2007).
Google Scholar
Burki, F. The eukaryotic tree of life from a global phylogenomic perspective. Cold Spring Harb. Perspect. Biol. 6, a016147 (2014).
Google Scholar
Waskom, M. et al. mwaskom/Seaborn: v0.8.1 (September 2017). Zenodo https://doi.org/10.5281/zenodo.883859 (2017).
Eddy, S. R. Accelerated profile HMM searches. PLoS Comput. Biol. 7, e1002195 (2011).
Google Scholar
Finn, R. D. et al. The Pfam protein families database: towards a more sustainable future. Nucleic Acids Res. 44, D279–D285 (2016).
Google Scholar
Letunic, I. & Bork, P. 20 years of the SMART protein domain annotation resource. Nucleic Acids Res. 46, D493–D496 (2018).
Google Scholar
Almagro Armenteros, J. J. et al. SignalP 5.0 improves signal peptide predictions using deep neural networks. Nat. Biotechnol. 37, 420–423 (2019).
Google Scholar
Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).
Google Scholar
Burns, J. A., Pittis, A. A. & Kim, E. Gene-based predictive models of trophic modes suggest Asgard archaea are not phagocytotic. Nat. Ecol. Evol. 2, 697–704 (2018).
Google Scholar
Emms, D. M. & Kelly, S. OrthoFinder: phylogenetic orthology inference for comparative genomics. Genome Biol. 20, 238 (2019).
Google Scholar
Altschul, S. F. et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25, 3389–3402 (1997).
Google Scholar
Hall, T. A. BioEdit: a user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. Nucleic Acids Symp. Ser. 41, 95–98 (1999).
Google Scholar
Minh, B. Q. et al. IQ-TREE 2: new models and efficient methods for phylogenetic inference in the genomic era. Mol. Biol. Evol. 37, 1530–1534 (2020).
Google Scholar
Whelan, S., Irisarri, I. & Burki, F. PREQUAL: detecting non-homologous characters in sets of unaligned homologous sequences. Bioinformatics 34, 3929–3930 (2018).
Google Scholar
Capella-Gutierrez, S., Silla-Martinez, J. M. & Gabaldon, T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 25, 1972–1973 (2009).
Google Scholar
Roure, B., Rodriguez-Ezpeleta, N. & Philippe, H. SCaFoS: a tool for selection, concatenation and fusion of sequences for phylogenomics. BMC Evol. Biol. 7, S2 (2007).
Google Scholar
Lartillot, N., Rodrigue, N., Stubbs, D. & Richer, J. PhyloBayes MPI: phylogenetic reconstruction with infinite mixtures of profiles in a parallel environment. Syst. Biol. 62, 611–615 (2013).
Google Scholar
Dayhoff, M., Schwartz, R. & Orcutt, B. in Atlas of Protein Sequence and Structure (ed. Dayhoff, M.) 345–352 (National Biomedical Research Foundation, 1978).
Susko, E. & Roger, A. J. On reduced amino acid alphabets for phylogenetic inference. Mol. Biol. Evol. 24, 2139–2150 (2007).
Google Scholar
Lartillot, N. & Philippe, H. A Bayesian mixture model for across-site heterogeneities in the amino-acid replacement process. Mol. Biol. Evol. 21, 1095–1109 (2004).
Google Scholar
Quang le, S., Gascuel, O. & Lartillot, N. Empirical profile mixture models for phylogenetic reconstruction. Bioinformatics 24, 2317–2323 (2008).
Google Scholar
Wang, H. C., Minh, B. Q., Susko, E. & Roger, A. J. Modeling site heterogeneity with posterior mean site frequency profiles accelerates accurate phylogenomic estimation. Syst. Biol. 67, 216–235 (2018).
Google Scholar
Kück, P. & Struck, T. H. BaCoCa—a heuristic software tool for the parallel assessment of sequence biases in hundreds of gene and taxon partitions. Mol. Phylogenet. Evol. 70, 94–98 (2014).
Google Scholar
Shimodaira, H. An approximately unbiased test of phylogenetic tree selection. Syst. Biol. 51, 492–508 (2002).
Google Scholar
Kumar, S., Stecher, G. & Tamura, K. MEGA7: molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol. Biol. Evol. 33, 1870–1874 (2016).
Google Scholar
Bankevich, A. et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J. Comput. Biol. 19, 455–477 (2012).
Google Scholar
Dierckxsens, N., Mardulyn, P. & Smits, G. NOVOPlasty: de novo assembly of organelle genomes from whole genome data. Nucleic Acids Res. 45, e18 (2017).
Kuznetsov, A. & Bollin, C. J. in Multiple Sequence Alignment (ed. Katoh, K.) 261–295 (Springer, 2021).
Lohse, M., Drechsel, O., Kahlau, S. & Bock, R. OrganellarGenomeDRAW—a suite of tools for generating physical maps of plastid and mitochondrial genomes and visualizing expression data sets. Nucleic Acids Res. 41, W575–W581 (2013).
Google Scholar
Johnson, P. Z., Kasprzak, W. K., Shapiro, B. A. & Simon, A. E. RNA2Drawer: geometrically strict drawing of nucleic acid structures with graphical structure editing and highlighting of complementary subsequences. RNA Biol. 16, 1667–1671 (2019).
Google Scholar
Burger, G., Gray, M. W., Forget, L. & Lang, B. F. Strikingly bacteria-like and gene-rich mitochondrial genomes throughout jakobid protists. Genome Biol. Evol. 5, 418–438 (2013).
Google Scholar
Criscuolo, A. & Gribaldo, S. BMGE (Block Mapping and Gathering with Entropy): a new software for selection of phylogenetic informative regions from multiple sequence alignments. BMC Evol. Biol. 10, 210 (2010).
Google Scholar
Zhang, D. et al. PhyloSuite: an integrated and scalable desktop platform for streamlined molecular sequence data management and evolutionary phylogenetics studies. Mol. Ecol. Resour. 20, 348–355 (2020).
Google Scholar
Nguyen, L.-T., Schmidt, H. A., von Haeseler, A. & Minh, B. Q. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274 (2015).
Google Scholar
Ibarbalz, F. M. et al. Global trends in marine plankton diversity across kingdoms of life. Cell 179, 1084–1097 (2019).
Google Scholar
Massana, R. et al. Marine protist diversity in European coastal waters and sediments as revealed by high-throughput sequencing. Environ. Microbiol. 17, 4035–4049 (2015).
Google Scholar
Gendron, E. M. S., Darcy, J. L., Hell, K. & Schmidt, S. K. Structure of bacterial and eukaryote communities reflect in situ controls on community assembly in a high-alpine lake. J. Microbiol. 57, 852–864 (2019).
Google Scholar
Minerovic, A. D. et al. 18S-V9 DNA metabarcoding detects the effect of water-quality impairment. Ecol. Indic. 113, 106225 (2020).
Google Scholar
Pearman, J. K. et al. Cross-shelf investigation of coral reef cryptic benthic organisms reveals diversity patterns of the hidden majority. Sci. Rep. 8, 8090 (2018).
Google Scholar
Rodas, A. M. et al. Eukaryotic plankton communities across reef environments in Bocas del Toro Archipelago, Panamá. Coral Reefs 39, 1453–1467 (2020).
Google Scholar
Schoenle, A. et al. High and specific diversity of protists in the deep-sea basins dominated by diplonemids, kinetoplastids, ciliates and foraminiferans. Commun. Biol. 4, 501 (2021).
Google Scholar
Schulhof, M. A. et al. Sierra Nevada mountain lake microbial communities are structured by temperature, resources and geographic location. Mol. Ecol. 29, 2080–2093 (2020).
Google Scholar
Yi, Z. et al. High-throughput sequencing of microbial eukaryotes in Lake Baikal reveals ecologically differentiated communities and novel evolutionary radiations. FEMS Microbiol. Ecol. 93, fix073 (2017).
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