Muscatine, L. & Porter, J. W. Reef corals: mutualistic symbioses adapted to nutrient-poor environments. Bioscience 27, 454–460 (1977).
Bellwood, D. R., Hughes, T. P., Folke, C. & Nyström, M. Confronting the coral reef crisis. Nature 429, 827–833 (2004).
Rivera, H. E. & Davies, S. W. Symbiosis maintenance in the facultative coral, Oculina arbuscula, relies on nitrogen cycling, cell cycle modulation, and immunity. Sci. Rep. 11, 21226 (2021).
Aichelman, H. E., Zimmerman, R. C. & Barshis, D. J. Adaptive signatures in thermal performance of the temperate coral Astrangia poculata. J. Exp. Biol. 222, jeb189225 (2019).
Fine, M., Zibrowius, H. & Loya, Y. Oculina patagonica: a non-lessepsian scleractinian coral invading the Mediterranean Sea. Mar. Biol. 138, 1195–1203 (2001).
Shemesh, T. et al. The effects of elevated temperatures on the reproductive biology of a mediterranean coral, Oculina patagonica. Oceans 5, 758–769 (2024).
Reaka-Kudla, M. L. in Biodiversity II: Understanding and Protecting our Biological Resources (eds Reaka-Kudla, M. L., Wilson, D. E. & Wilson, E. O.) 83–108 (1997).
Gates, R. D., Baghdasarian, G. & Muscatine, L. Temperature stress causes host cell detachment in symbiotic cnidarians: implications for coral bleaching. Biol. Bull. 182, 324–332 (1992).
Hughes, T. P. et al. Global warming and recurrent mass bleaching of corals. Nature 543, 373–377 (2017).
Shenkar, N., Fine, M. & Loya, Y. Size matters: bleaching dynamics of the coral Oculina patagonica. Mar. Ecol. Prog. Ser. 294, 181–188 (2005).
Leydet, K. P. & Hellberg, M. E. The invasive coral Oculina patagonica has not been recently introduced to the Mediterranean from the western Atlantic. BMC Evol. Biol. 15, 79 (2015).
Rubio-Portillo, E. et al. Eukarya associated with the stony coral Oculina patagonica from the Mediterranean Sea. Mar. Genomics 17, 17–23 (2014).
Rubio-Portillo, E., Vázquez-Luis, M., Valle, C., Izquierdo-Muñoz, A. & Ramos-Esplá, A. A. Growth and bleaching of the coral Oculina patagonica under different environmental conditions in the western Mediterranean Sea. Mar. Biol. 161, 2333–2343 (2014).
Rodolfo-Metalpa, R., Reynaud, S., Allemand, D. & Ferrier-Pagès, C. Temporal and depth responses of two temperate corals, Cladocora caespitosa and Oculina patagonica, from the North Mediterranean Sea. Mar. Ecol. Prog. Ser. 369, 103–114 (2008).
Serrano, E., Ribes, M. & Coma, R. Demographics of the zooxanthellate coral Oculina patagonica along the Mediterranean Iberian coast in relation to environmental parameters. Sci. Total Environ. 634, 1580–1592 (2018).
Salomidi, M., Katsanevakis, S., Issaris, Y., Tsiamis, K. & Katsiaras, N. Anthropogenic disturbance of coastal habitats promotes the spread of the introduced scleractinian coral Oculina patagonica in the Mediterranean Sea. Biol. Invasions 15, 1961–1971 (2013).
Terrón-Sigler, A., Casado-Amezúa, P. & Torre, F. E. Abundance and distribution of the rapid expansive coral Oculina patagonica in the Northern Alborán Sea (Western Mediterranean). Mar. Biodivers. Rec. 8, e45 (2015).
Pastor, F., Valiente, J. A. & Palau, J. L. Sea surface temperature in the Mediterranean: trends and spatial patterns (1982–2016). Pure Appl. Geophys. 175, 4017–4029 (2018).
Martinez, S., Bellworthy, J., Ferrier-Pagès, C. & Mass, T. Selection of mesophotic habitats by Oculina patagonica in the Eastern Mediterranean Sea following global warming. Sci. Rep. 11, 18134 (2021).
Fuller, Z. L. et al. Population genetics of the coroal Acropora millepora: toward genomic prediction of bleaching. Science 369, eaba4674 (2020).
Stankiewicz, K. H. et al. Genomic comparison of the temperate coral Astrangia poculata with tropical corals yields insights into winter quiescence, innate immunity, and sexual reproduction. G3 15, jkaf033 (2025).
Ying, H. et al. Comparative genomics reveals the distinct evolutionary trajectories of the robust and complex coral lineages. Genome Biol. 19, 175 (2018).
Fletcher, C. & da Conceicoa, L. P. The genome sequence of the starlet sea anemone, Nematostella vectensis (Stephenson, 1935). Wellcome Open Res. 8, 79 (2023).
Hu, M., Zheng, X., Fan, C.-M. & Zheng, Y. Lineage dynamics of the endosymbiotic cell type in the soft coral Xenia. Nature 582, 534–538 (2020).
Simakov, O. et al. Deeply conserved synteny and the evolution of metazoan chromosomes. Sci. Adv. 8, eabi5884 (2022).
Saunders, P. A. & Muyle, A. Sex chromosome evolution: hallmarks and question marks. Mol. Biol. Evol. 41, msae218 (2024).
Mao, Y. & Satoh, N. A likely ancient genome duplication in the speciose reef-building coral genus, Acropora. iScience 13, 20–32 (2019).
Noel, B. et al. Pervasive tandem duplications and convergent evolution shape coral genomes. Genome Biol. 24, 123 (2023).
Voolstra, C. R. et al. Comparative analysis of the genomes of Stylophora pistillata and Acropora digitifera provides evidence for extensive differences between species of corals. Sci. Rep. 7, 17583 (2017).
García-Castro, H. et al. ACME dissociation: a versatile cell fixation–dissociation method for single-cell transcriptomics. Genome Biol. 22, 89 (2021).
Najle, S. R. et al. Stepwise emergence of the neuronal gene expression program in early animal evolution. Cell 186, 4676–4693.e29 (2023).
Baran, Y. et al. MetaCell: analysis of single-cell RNA-seq data using K-nn graph partitions. Genome Biol. 20, 206 (2019).
Sebé-Pedrós, A. et al. Cnidarian cell type diversity and regulation revealed by whole-organism single-cell RNA-seq. Cell 173, 1520–1534.e20 (2018).
Steger, J. et al. Single-cell transcriptomics identifies conserved regulators of neuroglandular lineages. Cell Rep. 40, 111370 (2022).
Levy, S. et al. A stony coral cell atlas illuminates the molecular and cellular basis of coral symbiosis, calcification, and immunity. Cell 184, 2973–2987.e18 (2021).
Irimia, M. et al. Extensive conservation of ancient microsynteny across metazoans due to cis-regulatory constraints. Genome Res. 22, 2356–67 (2012).
LaJeunesse, T. C. et al. Systematic revision of Symbiodiniaceae highlights the antiquity and diversity of coral endosymbionts. Curr. Biol. 28, 2570–2580.e6 (2018).
Valadez-Ingersoll, M. et al. Cell type-specific immune regulation under symbiosis in a facultatively symbiotic coral. ISME J. 19, wraf132 (2025).
Pinzón, J. H. et al. Whole transcriptome analysis reveals changes in expression of immune-related genes during and after bleaching in a reef-building coral. R. Soc. Open. Sci. 2, 140214 (2015).
Wall, C. B. et al. The effects of environmental history and thermal stress on coral physiology and immunity. Mar. Biol. 165, 56 (2018).
Barott, K. L., Venn, A. A., Perez, S. O., Tambutté, S. & Tresguerres, M. Coral host cells acidify symbiotic algal microenvironment to promote photosynthesis. Proc. Natl Acad. Sci. USA 112, 607–612 (2015).
Matz, M. V. Not-so-mutually beneficial coral symbiosis. Curr. Biol. 34, R798–R801 (2024).
Thies, A. B., Quijada-Rodriguez, A. R., Zhouyao, H., Weihrauch, D. & Tresguerres, M. A Rhesus channel in the coral symbiosome membrane suggests a novel mechanism to regulate NH3 and CO2 delivery to algal symbionts. Sci. Adv. 8, 303 (2022).
Griffin, M. J., Wong, R. H. F., Pandya, N. & Sul, H. S. Direct interaction between USF and SREBP-1c mediates synergistic activation of the fatty-acid synthase promoter. J. Biol. Chem. 282, 5453–5467 (2007).
Nardone, C. et al. A central role for regulated protein stability in the control of TFE3 and MITF by nutrients. Mol. Cell 83, 57–73.e9 (2023).
Voss, P. A. et al. Host nutrient sensing is mediated by mTOR signaling in cnidarian-dinoflagellate symbiosis. Curr. Biol. 33, 3634–3647.e5 (2023).
Hara, K. et al. Raptor, a binding partner of target of rapamycin (TOR), mediates TOR action. Cell 110, 177–189 (2002).
Kim, D.-H. et al. mTOR interacts with Raptor to form a nutrient-sensitive complex that signals to the cell growth machinery. Cell 110, 163–175 (2002).
Ramírez, J. A. et al. Folliculin interacting protein 1 maintains metabolic homeostasis during B cell development by modulating AMPK, mTORC1, and TFE3. J. Immunol. 203, 2899–2908 (2019).
Saxton, R. A. & Sabatini, D. M. mTOR signaling in growth, metabolism, and disease. Cell 168, 960–976 (2017).
Alderton, W. K., Cooper, C. E. & Knowles, R. G. Nitric oxide synthases: structure, function and inhibition. Biochem. J. 357, 593–615 (2001).
Soni, L. E., Warren, C. M., Bucci, C., Orten, D. J. & Hasson, T. The unconventional myosin-VIIa associates with lysosomes. Cell Motil. Cytoskeleton 62, 13–26 (2005).
Fric, J. et al. NFAT control of innate immunity. Blood 120, 1380–1389 (2012).
Baumgarten, S. et al. The genome of Aiptasia, a sea anemone model for coral symbiosis. Proc. Natl Acad. Sci. USA 112, 11893–11898 (2015).
Hsieh, T.-H. S. et al. Resolving the 3D landscape of transcription-linked mammalian chromatin folding. Mol. Cell 78, 539–553.e8 (2020).
Krietenstein, N. et al. Ultrastructural details of mammalian chromosome architecture. Mol. Cell 78, 554–565.e7 (2020).
Durand, N. C. et al. Juicer provides a one-click system for analyzing loop-resolution Hi-C experiments. Cell Syst. 3, 95–98 (2016).
Dudchenko, O. et al. De novo assembly of the Aedes aegypti genome using Hi-C yields chromosome-length scaffolds. Science 356, 92–95 (2017).
Mapleson, D., Garcia Accinelli, G., Kettleborough, G., Wright, J. & Clavijo, B. J. KAT: a K-mer analysis toolkit to quality control NGS datasets and genome assemblies. Bioinformatics 33, 574–576 (2017).
Brůna, T., Hoff, K. J., Lomsadze, A., Stanke, M. & Borodovsky, M. BRAKER2: automatic eukaryotic genome annotation with GeneMark-EP+ and AUGUSTUS supported by a protein database. NAR Genomics Bioinformatics 3, lqaa108 (2021).
Stanke, M. et al. AUGUSTUS: ab initio prediction of alternative transcripts. Nucleic Acids Res. 34, W435 (2006).
Pertea, M. et al. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat. Biotechnol. 33, 290–295 (2015).
Gremme, G., Brendel, V., Sparks, M. E. & Kurtz, S. Engineering a software tool for gene structure prediction in higher organisms. Inf. Softw. Technol. 47, 965–978 (2005).
Venturini, L., Caim, S., Kaithakottil, G. G., Mapleson, D. L. & Swarbreck, D. Leveraging multiple transcriptome assembly methods for improved gene structure annotation. Gigascience 7, giy093 (2018).
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015).
Suzek, B. E., Wang, Y., Huang, H., McGarvey, P. B. & Wu, C. H. UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches. Bioinformatics 31, 926–932 (2015).
Ou, S. et al. Benchmarking transposable element annotation methods for creation of a streamlined, comprehensive pipeline. Genome Biol. 20, 275 (2019).
Pertea, G. & Pertea, M. GFF utilities: GffRead and GffCompare. F1000Res 9, 304 (2020).
Waterhouse, R. M. et al. BUSCO applications from quality assessments to gene prediction and phylogenomics. Mol. Biol. Evol. 35, 543–548 (2018).
Derelle, R., Philippe, H. & Colbourne, J. K. Broccoli: combining phylogenetic and network analyses for orthology assignment. Mol. Biol. Evol. 37, 3389–3396 (2020).
Punta, M. et al. The Pfam protein families database. Nucleic Acids Res. 40, D290–301 (2012).
Mistry, J., Finn, R. D., Eddy, S. R., Bateman, A. & Punta, M. Challenges in homology search: HMMER3 and convergent evolution of coiled-coil regions. Nucleic Acids Res. 41, e121 (2013).
Enright, A. J., Van Dongen, S. & Ouzounis, C. A. An efficient algorithm for large-scale detection of protein families. Nucleic Acids Res. 30, 1575–1584 (2002).
Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).
Steenwyk, J. L., Buida, T. J., Li, Y., Shen, X.-X. & Rokas, A. ClipKIT: a multiple sequence alignment trimming software for accurate phylogenomic inference. PLoS Biol. 18, e3001007 (2020).
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).
Kalyaanamoorthy, S., Minh, B. Q., Wong, T. K. F., von Haeseler, A. & Jermiin, L. S. ModelFinder: fast model selection for accurate phylogenetic estimates. Nat. Methods 14, 587–589 (2017).
Hoang, D. T., Chernomor, O., von Haeseler, A., Minh, B. Q. & Vinh, L. S. UFBoot2: improving the ultrafast bootstrap approximation. Mol. Biol. Evol. 35, 518–522 (2018).
Grau-Bové, X. & Sebé-Pedrós, A. Orthology clusters from gene trees with Possvm. Mol. Biol. Evol. 38, 5204–5208 (2021).
Blake, J. A. et al. Mouse Genome Database (MGD): knowledgebase for mouse–human comparative biology. Nucleic Acids Res. 49, D981–D987 (2021).
Kanehisa, M. & Goto, S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 28, 27–30 (2000).
Kanehisa, M., Furumichi, M., Sato, Y., Kawashima, M. & Ishiguro-Watanabe, M. KEGG for taxonomy-based analysis of pathways and genomes. Nucleic Acids Res. 51, D587–D592 (2023).
Krogh, A., Larsson, B., von Heijne, G. & Sonnhammer, E. L. Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J. Mol. Biol. 305, 567–580 (2001).
Almagro Armenteros, J. J. et al. SignalP 5.0 improves signal peptide predictions using deep neural networks. Nat. Biotechnol. 37, 420–423 (2019).
Csűrös, M. & Miklós, I. in Research in Computational Molecular Biology (eds. Apostolico, A. et al.) 206–220 (Springer, 2006).
Csurös, M. Count: evolutionary analysis of phylogenetic profiles with parsimony and likelihood. Bioinformatics 26, 1910–1912 (2010).
Lawrence, M. et al. Software for computing and annotating genomic ranges. PLoS Comput. Biol. 9, e1003118 (2013).
Lawrence, M., Gentleman, R. & Carey, V. rtracklayer: an R package for interfacing with genome browsers. Bioinformatics 25, 1841–1842 (2009).
Armstrong, J. et al. Progressive Cactus is a multiple-genome aligner for the thousand-genome era. Nature 587, 246–251 (2020).
Hickey, G., Paten, B., Earl, D., Zerbino, D. & Haussler, D. HAL: a hierarchical format for storing and analyzing multiple genome alignments. Bioinformatics 29, 1341–1342 (2013).
Hubisz, M. J., Pollard, K. S. & Siepel, A. PHAST and RPHAST: phylogenetic analysis with space/time models. Brief. Bioinformatics 12, 41–51 (2011).
Pollard, K. S., Hubisz, M. J., Rosenbloom, K. R. & Siepel, A. Detection of nonneutral substitution rates on mammalian phylogenies. Genome Res. 20, 110–121 (2010).
Sensalari, C., Maere, S. & Lohaus, R. ksrates: positioning whole-genome duplications relative to speciation events in KS distributions. Bioinformatics 38, 530–532 (2022).
Zwaenepoel, A. & Van de Peer, Y. Inference of ancient whole-genome duplications and the evolution of gene duplication and loss rates. Mol. Biol. Evol. 36, 1384–1404 (2019).
Edgar, R. C. MUSCLE: a multiple sequence alignment method with reduced time and space complexity. BMC Bioinformatics 5, 113 (2004).
Proost, S. et al. i-ADHoRe 3.0—fast and sensitive detection of genomic homology in extremely large data sets. Nucleic Acids Res. 40, e11 (2012).
Gehring, J., Hwee Park, J., Chen, S., Thomson, M. & Pachter, L. Highly multiplexed single-cell RNA-seq by DNA oligonucleotide tagging of cellular proteins. Nat. Biotechnol. 38, 35–38 (2020).
Keren-Shaul, H. et al. MARS-seq2.0: an experimental and analytical pipeline for indexed sorting combined with single-cell RNA sequencing. Nat. Protoc. 14, 1841–1862 (2019).
Zolotarov, G., Grau-Bové, X. & Sebé-Pedrós, A. GeneExt: a gene model extension tool for enhanced single-cell RNA-seq analysis. Preprint at bioRxiv https://doi.org/10.1101/2023.12.05.570120 (2023).
Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).
Chari, T. et al. Whole-animal multiplexed single-cell RNA-seq reveals transcriptional shifts across Clytia medusa cell types. Sci. Adv. 7, eabh1683 (2021).
Germain, P.-L., Lun, A., Garcia Meixide, C., Macnair, W. & Robinson, M. D. Doublet identification in single-cell sequencing data using scDblFinder. F1000Res 10, 979 (2022).
Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e21 (2019).
Hafemeister, C. & Satija, R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol. 20, 296 (2019).
Choudhary, S. & Satija, R. Comparison and evaluation of statistical error models for scRNA-seq. Genome Biol. 23, 27 (2022).
Traag, V. A., Waltman, L. & van Eck, N. J. From Louvain to Leiden: guaranteeing well-connected communities. Sci. Rep. 9, 5233–12 (2019).
Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).
Zhuang, H., Wang, H. & Ji, Z. findPC: an R package to automatically select the number of principal components in single-cell analysis. Bioinformatics 38, 2949–2951 (2022).
Büttner, M., Miao, Z., Wolf, F. A., Teichmann, S. A. & Theis, F. J. A test metric for assessing single-cell RNA-seq batch correction. Nat. Methods 16, 43–49 (2019).
Rousseeuw, P. J. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987).
Tournière, O. et al. NvPOU4/Brain3 functions as a terminal selector gene in the nervous system of the cnidarian Nematostella vectensis. Cell Rep. 30, 4473–4489.e5 (2020).
Büttner, M., Ostner, J., Müller, C. L., Theis, F. J. & Schubert, B. scCODA is a Bayesian model for compositional single-cell data analysis. Nat. Commun. 12, 6876 (2021).
Heumos, L. et al. Pertpy: an end-to-end framework for perturbation analysis. Preprint at bioRxiv https://doi.org/10.1101/2024.08.04.606516 (2024).
Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9, 559 (2008).
Langfelder, P., Zhang, B. & Horvath, S. Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R. Bioinformatics 24, 719–720 (2008).
Schliep, K. P. phangorn: phylogenetic analysis in R. Bioinformatics 27, 592–593 (2011).
Tirosh, I. & Barkai, N. Comparative analysis indicates regulatory neofunctionalization of yeast duplicates. Genome Biol. 8, R50 (2007).
Felsenstein, J. Phylogenies and the comparative method. Am. Nat. 125, 1–15 (1985).
Tarashansky, A. J., Xue, Y., Li, P., Quake, S. R. & Wang, B. Self-assembling manifolds in single-cell RNA sequencing data. eLife 8, e48994 (2019).
Tarashansky, A. J. et al. Mapping single-cell atlases throughout Metazoa unravels cell type evolution. eLife 10, e66747 (2021).
Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol 19, 15 (2018).
Alexa, A., Rahnenführer, J. & Lengauer, T. Improved scoring of functional groups from gene expression data by decorrelating GO graph structure. Bioinformatics 22, 1600–1607 (2006).
