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Evolutionary dynamics of genome size and content during the adaptive radiation of Heliconiini butterflies

Cicconardi, Francesco, Milanetti, Edoardo, Pinheiro de Castro, Erika C., Mazo-Vargas, Anyi, Van Belleghem, Steven M., Ruggieri, Angelo Alberto, Rastas, Pasi, Hanly, Joseph, Evans, Elizabeth, Jiggins, Chris D., Owen McMillan, W., Papa, Riccardo, Di Marino, Daniele, Martin, Arnaud, Montgomery, Stephen H.·2023·Nature Communications
DOI 10.1038/s41467-023-41412-5PMC PMC10497600

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Abstract

Heliconius butterflies, a speciose genus of Müllerian mimics, represent a classic example of an adaptive radiation that includes a range of derived dietary, life history, physiological and neural traits. However, key lineages within the genus, and across the broader Heliconiini tribe, lack genomic resources, limiting our understanding of how adaptive and neutral processes shaped genome evolution during their radiation. Here, we generate highly contiguous genome assemblies for nine Heliconiini, 29 additional reference-assembled genomes, and improve 10 existing assemblies. Altogether, we provide a dataset of annotated genomes for a total of 63 species, including 58 species within the Heliconiini tribe. We use this extensive dataset to generate a robust and dated heliconiine phylogeny, describe major patterns of introgression, explore the evolution of genome architecture, and the genomic basis of key innovations in this enigmatic group, including an assessment of the evolution of putative regulatory regions at the Heliconius stem. Our work illustrates how the increased resolution provided by such dense genomic sampling improves our power to generate and test gene-phenotype hypotheses, and precisely characterize how genomes evolve.

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  1. Stroud, J. T. & Losos, J. B. Ecological opportunity and adaptive radiation. Annu. Rev. Ecol. Evol. Syst. 47, 507–532 (2016). · DOI
  2. Erwin, D. H. A conceptual framework of evolutionary novelty and innovation. Biol. Rev. 96, 1–15 (2021). · DOI
  3. Cicconardi, F. et al. Genomic signature of shifts in selection in a sub-alpine ant and its physiological adaptations. Mol. Biol. Evol. 1–17 (2020) https://doi.org/10.1093/molbev/msaa076. · DOI
  4. Yuan, Y. et al. Comparative genomics provides insights into the aquatic adaptations of mammals. Proc. Natl Acad. Sci. USA 118, 1–9 (2021). · DOI
  5. Kozak, K. M., Joron, M., McMillan, W. O. & Jiggins, C. D. Rampant genome-wide admixture across the heliconius radiation. Genome Biol. Evol. 13, 1–17 (2021). · DOI
  6. Martin, S. H., Davey, J. W., Salazar, C. & Jiggins, C. D. Recombination rate variation shapes barriers to introgression across butterfly genomes. PLoS Biol. (2019) https://doi.org/10.1371/journal.pbio.2006288. · DOI
  7. Edelman, N. B. et al. Genomic architecture and introgression shape a butterfly radiation. Science 599, 594–599 (2019). · DOI
  8. Kozak, K. M. et al. Multilocus species trees show the recent adaptive radiation of the mimetic heliconius butterflies. Syst. Biol. 64, 505–524 (2015). · DOI
  9. de Castro, É. C. P. et al. Sequestration and biosynthesis of cyanogenic glucosides in passion vine butterflies and consequences for the diversification of their host plants. Ecol. Evol. 9, 5079–5093 (2019). · DOI
  10. Young, F. J. & Montgomery, S. H. Pollen feeding in Heliconius butterflies: the singular evolution of an adaptive suite. Proc. R. Soc. B Biol. Sci. 287, 20201304 (2020). · DOI
  11. Montgomery, S. H., Merrill, R. M. & Ott, S. R. Brain composition in Heliconius butterflies, posteclosion growth and experience-dependent neuropil plasticity. J. Comp. Neurol. 524, 1747–1769 (2016). · DOI
  12. Hawornwattana, Y. U. T., Eixas, F. E. A. S., Ang, Z. I. Y. & Allet, J. A. M. Full-likelihood genomic analysis clarifies a complex history of species divergence and introgression: the example of the erato − sara group of Heliconius butterflies. Syst. Biol. 71, 1159–1177 (2022). · DOI
  13. Thawornwattana, Y., Seixas, F. A., Yang, Z. & Mallet, J. Full-likelihood genomic analysis clarifies a complex history of species divergence and introgression: the example of the erato-sara group of Heliconius butterflies. Syst. Biol. 71, 1159–1177 (2022). · DOI
  14. Suvorov, A. et al. Widespread introgression across a phylogeny of 155 Drosophila genomes. Curr. Biol. 1–13 (2021) https://doi.org/10.1016/j.cub.2021.10.052. · DOI
  15. Walters, J. R., Corbins, C., Hardcastle, T. J. & Jiggins, C. D. Evaluating female remating rates in light of spermatophore degradation in Heliconius butterflies: pupal-mating monandry versus adult-mating polyandry. Ecol. Entomol. 37, 257–268 (2012). · DOI
  16. Thurman, T. J., Brodie, E., Evans, E. & McMillan, W. O. Facultative pupal mating in Heliconius erato: Implications for mate choice, female preference, and speciation. Ecol. Evol. 8, 1882–1889 (2018). · DOI
  17. Thawornwattana, Y., Seixas, F. A., Yang, Z. & Mallet, J. Major patterns in the introgression history of Heliconius butterflies. bioRxiv, 1–20 (2023). · DOI
  18. Kapusta, A., Suh, A. & Feschotte, C. Dynamics of genome size evolution in birds and mammals. Proc. Natl. Acad. Sci. USA. (2017) https://doi.org/10.1073/pnas.1616702114. · DOI · Paper page
  19. Ruggieri, A. A. et al. A butterfly pan-genome reveals that a large amount of structural variation underlies the evolution of chromatin accessibility. Genome Res. 32, 1862–1875 (2022). · DOI
  20. Cicconardi, F. et al. Chromosome fusion affects genetic diversity and evolutionary turnover of functional loci but consistently depends on chromosome size. Mol. Biol. Evol. 38, 4449–4462 (2021). · DOI
  21. Sun, C. et al. Genus-wide characterization of bumblebee genomes provides insights into their evolution and variation in ecological and behavioral traits. Mol. Biol. Evol. 38, 486–501 (2021). · DOI
  22. Neafsey, D. E. et al. Highly evolvable malaria vectors: the genomes of 16 Anopheles mosquitoes. Science 347, 1258522 (2015). · DOI
  23. Castro, É. C. P. De, Musgrove, J., Bak, S., Mcmillan, W. O. & Jiggins, C. D. Phenotypic plasticity in chemical defence of butterflies allows usage of diverse host plants. Biol. Lett. 17, 20200863 (2021). · DOI
  24. Pinheiro de Castro, É. C. et al. The dynamics of cyanide defences in the life cycle of an aposematic butterfly: Biosynthesis versus sequestration. Insect Biochem. Mol. Biol. 116, 103259 (2020). · DOI
  25. Du, M. et al. Identification of lipases involved in PBAN stimulated Pheromone production in Bombyx mori using the DGE and RNAi approaches. PLoS One 7, e31045 (2012). · DOI
  26. Couto, A. et al. Rapid expansion and visual specialisation of learning and memory centres in the brains of Heliconiini butterflies. Nat. Commun. 14, 4024 (2023). · DOI
  27. Lage, J. L. Da, Thomas, G. W. C., Bonneau, M. & Courtier-Orgogozo, V. Evolution of salivary glue genes in Drosophila species. BMC Evol. Biol. 9, 36 (2018).
  28. Opitz, S. E. W. & Müller, C. Plant chemistry and insect sequestration. Chemoecology 19, 117–154 (2009). · DOI · Paper page
  29. Sung, E. J. et al. Cytokine signaling through Drosophila Mthl10 ties lifespan to environmental stress. Proc. Natl. Acad. Sci. USA. (2017) https://doi.org/10.1073/pnas.1712453115. · DOI
  30. Wu, L. et al. CYP303A1 has a conserved function in adult eclosion in Locusta migratoria and Drosophila melanogaster. Insect Biochem. Mol. Biol. 113, 103210 (2019). · DOI
  31. Tang, B., Wang, S. & Zhang, F. Two storage hexamerins from the beet armyworm Spodoptera exigua: Cloning, characterization and the effect of gene silencing on survival. BMC Mol. Biol. 11, 65 (2010). · DOI
  32. Portin, P. & Portin, P. General outlines of the molecular genetics of the Notch signalling pathway in Drosophila melanogaster: a review. Hereditas 136, 89–96 (2002). · DOI
  33. Li, X., Xie, Y. & Zhu, S. Notch maintains Drosophila type II neuroblasts by suppressing expression of the fez transcription factor earmuff. Dev 143, 2511–2521 (2016).
  34. Sackton, T. B. et al. Convergent regulatory evolution and the origin of flightlessness in palaeognathous birds. Science 364, 74–78 (2019). · DOI · Paper page
  35. Lin, Q. et al. The seahorse genome and the evolution of its specialized morphology. Nature 540, 395–399 (2016). · DOI
  36. Snetkova, V., Pennacchio, L. A., Visel, A. & Dickel, D. E. Perfect and imperfect views of ultraconserved sequences. Nat. Rev. Genet. 23, 182–194 (2022). · DOI
  37. Sackton, T. B. et al. Convergent regulatory evolution and loss of flight in paleognathous birds. Science 364, 74–78 (2019). · DOI · Paper page
  38. 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). · DOI · Paper page
  39. Van Belleghem, S. M. et al. High level of novelty under the hood of convergent evolution. Science 379, 1043–1049 (2023). · DOI
  40. Han Yan et al. PhyloAcc-GT: a Bayesian method for inferring patterns of substitution rate shifts and associations with binary traits under gene tree discordance. Mol. Biol. Evol. msad195, https://doi.org/10.1093/molbev/msad195 (2022). · DOI · Paper page
  41. Roblodowski, C. & He, Q. Drosophila Dunc-115 mediates axon projection through actin binding. Invertebr. Neurosci. 17, 2 (2017). · DOI
  42. Frank, C. A. & James, T. D. Homeostatic control of Drosophila neuromuscular junction function. Synapse 74, e22133 (2020) https://doi.org/10.1002/syn.22133. · DOI
  43. Heymann, C. et al. Molecular insights into the axon guidance molecules Sidestep and Beaten path. Front. Physiol. 13, https://doi.org/10.3389/fphys.2022.1057413 (2022). · DOI
  44. Chen, K., Richlitzki, A., Featherstone, D. E., Schwärzel, M. & Richmond, J. E. Tomosyn-dependent regulation of synaptic transmission is required for a late phase of associative odor memory. Proc. Natl Acad. Sci. USA 108, 18482–18487 (2011) https://doi.org/10.1073/pnas.1110184108. · DOI
  45. Protection, N., Drosophila, A., Hospital, W., Shcool, H. M. & Hughes, H. An evolutionarily conserved role of presenilin in neuronal protection in the aging Drosophila brain. Genetics 206, 1479–1493 (2017). · DOI
  46. Sun, J., Zhang, J., Wang, D. & Shen, J. The transcription factor Spalt and human homologue SALL4 induce cell invasion via the dMyc-JNK pathway in Drosophila. Biol. Open 9, bio048850 (2020). · DOI
  47. Closser, M. et al. Article An expansion of the non-coding genome and its regulatory potential underlies vertebrate neuronal diversity ll Article An expansion of the non-coding genome and its regulatory potential underlies vertebrate neuronal diversity. Neuron 110, 70–85.e6 (2022). · DOI
  48. Link, B. A. The roles of hippo signaling transducers Yap and Taz in chromatin remodeling. Cells. 8, 502 (2019). · DOI
  49. Stem, G. & Progeny, C. The Osa-containing SWI/SNF chromatin-remodeling complex is required in the germline differentiation niche for germline stem cell progeny differentiation. Genes 12, 363 (2021). · DOI
  50. Chubak, M. C. et al. Individual components of the SWI/SNF chromatin remodelling complex have distinct roles in memory neurons of the Drosophila mushroom body. Dis. Model Mech. https://doi.org/10.1242/dmm.037325 (2019). · DOI
  51. Farris, S. M. Evolution of complex higher brain centers and behaviors: Behavioral correlates of mushroom body elaboration in insects. Brain. Behav. Evol. 82, 9–18 (2013). · DOI
  52. Sahu, M. R. & Mondal, A. C. Neuronal Hippo signaling: From development to diseases. Dev. Neurobiol. 81, 92–109 (2021). · DOI
  53. Kaya-çopur, A. et al. The hippo pathway controls myofibril assembly and muscle fiber growth by regulating sarcomeric gene expression. Elife 10, 1–34 (2021). · DOI
  54. Abeysundara, N., Simmonds, A. J. & Hughes, S. C. Moesin is involved in polarity maintenance and cortical remodeling during asymmetric cell division. Mol. Biol. Cell 29, 419–434 (2018). · DOI
  55. Wang, X., Zhang, Y. & Blair, S. S. Fat-regulated adaptor protein Dlish binds the growth suppressor Expanded and controls its stability and ubiquitination. Proc. Natl Acad. Sci. USA 116, 1319–1324 (2019). · DOI
  56. Bahrampour, S. & Thor, S. Ctr9, a key component of the paf1 complex, affects proliferation and terminal differentiation in the developing drosophila nervous system. G3 Genes Genomes Genet. 6, 3229–3239 (2016). · DOI
  57. Loyer, N. & Januschke, J. Where does asymmetry come from? Illustrating principles of polarity and asymmetry establishment in Drosophila neuroblasts. Curr. Opin. Cell Biol. 62, 70–77 (2020). · DOI
  58. Blair, S. & McNeill, H. Big roles for fat cadherins. Curr. Opin. Cell Biol. 51, 73–80 (2018). · DOI
  59. Yildirim, K., Petri, J., Kottmeier, R. & Klämbt, C. Drosophila glia: few cell types and many conserved functions. Glia 67, 5–26 (2019). · DOI
  60. Smith, G. et al. Evolutionary and structural analyses uncover a role for solvent interactions in the diversification of cocoonases in butterflies. Proc. R. Soc. B Biol. Sci. 285, 20172037 (2018). · DOI
  61. Gai, T. et al. Cocoonase is indispensable for Lepidoptera insects breaking the sealed cocoon. PLoS Genet. 16, 1–16 (2020). · DOI
  62. Gerasimavicius, L., Livesey, B. J. & Marsh, J. A. Loss-of-function, gain-of-function and dominant-negative mutations have profoundly different effects on protein structure. Nat. Commun. 1–15, https://doi.org/10.1038/s41467-022-31686-6 (2022). · DOI
  63. Kaplow, I. M. et al. Relating enhancer genetic variation across mammals to complex phenotypes using machine learning. Science 380, 2022.08.26.505436 (2023). · DOI
  64. Cheng, H., Concepcion, G. T., Feng, X., Zhang, H. & Li, H. Haplotype-resolved de novo assembly using phased assembly graphs with hifiasm. Nat. Methods 18, 170–175 (2021). · DOI · Paper page
  65. Koren, S. et al. Canu: scalable and accurate long- ­ ‐ read assembly via adaptive k - ­ ‐ mer weighting and repeat separation. Genome Res. 1–35 https://doi.org/10.1101/gr.215087.116.Freely (2016). · DOI
  66. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013). · DOI · Paper page
  67. Walker, B. J. et al. Pilon: an integrated tool for comprehensive microbial variant detection and genome assembly improvement. PLoS One 9, e112963 (2014). · DOI
  68. Roach, M. J., Schmidt, S. A. & Borneman, A. R. Purge haplotigs: allelic contig reassignment for third-gen diploid genome assemblies. BMC Bioinforma. 19, 1–10 (2018). · DOI
  69. Zhu, B. H. et al. P_RNA_scaffolder: a fast and accurate genome scaffolder using paired-end RNA-sequencing reads. BMC Genomics 19, 1–13 (2018). · DOI
  70. Qin, M. et al. LRScaf: improving draft genomes using long noisy reads. BMC Genomics 20, 955 (2019). · DOI
  71. Xu, G. C. et al. LR-Gapcloser: a tiling path-based gap closer that uses long reads to complete genome assembly. Gigascience 8, 1–14 (2018).
  72. Tang, H. et al. ALLMAPS: robust scaffold ordering based on multiple maps. Genome Biol. 16, 1–15 (2015). · DOI
  73. Zheng, G. X. Y. et al. Haplotyping germline and cancer genomes with high-throughput linked-read sequencing. Nat. Biotechnol. 34, 303–311 (2016). · DOI
  74. Simão, 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). · DOI · Paper page
  75. Jackman, S. D. et al. Tigmint: correcting assembly errors using linked reads from large molecules. BMC Bioinforma. 19, 1–10 (2018). · DOI
  76. Yeo, S., Coombe, L., Warren, R. L., Chu, J. & Birol, I. ARCS: Scaffolding genome drafts with linked reads. Bioinformatics 34, 725–731 (2018). · DOI
  77. Lischer, H. E. L. & Shimizu, K. K. Reference-guided de novo assembly approach improves genome reconstruction for related species. BMC Bioinformatics 1–12, https://doi.org/10.1186/s12859-017-1911-6 (2017). · DOI
  78. Li, H. Minimap2: Pairwise alignment for nucleotide sequences. Bioinformatics 34, 3094–3100 (2018). · DOI · Paper page
  79. Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009). · DOI · Paper page
  80. Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009). · DOI · Paper page
  81. Bankevich, A. et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J. Comput. Biol. 19, 455–477 (2012). · DOI
  82. Schatz, M. C. et al. Hawkeye and AMOS: visualizing and assessing the quality of genome assemblies. Brief. Bioinform. 14, 213–224 (2013). · DOI
  83. Pop, M., Phillippy, A., Delcher, A. L. & Salzberg, S. L. Comparative genome assembly. Brief. Bioinform. https://doi.org/10.1093/bib/5.3.237 (2004). · DOI
  84. McKenna, A. et al. The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. https://doi.org/10.1101/gr.107524.110 (2010). · DOI · Paper page
  85. Luo, R. et al. SOAPdenovo2: an empirically improved memory-efficient short-read de novo assembler. Gigascience 1, 18 (2012). · DOI
  86. Kim, D., Paggi, J. M., Park, C., Bennett, C. & Salzberg, S. L. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat. Biotechnol. 37, 907–915 (2019). · DOI · Paper page
  87. Alonge, M. et al. RaGOO: fast and accurate reference-guided scaffolding of draft genomes. Genome Biol. 20, 1–17 (2019). · DOI
  88. Paulino, D. et al. Sealer: a scalable gap-closing application for finishing draft genomes. BMC Bioinforma. 16, 1–8 (2015). · DOI
  89. Seixas, F. A., Edelman, N. B. & Mallet, J. Synteny-based genome assembly for 16 species of Heliconius butterflies, and an assessment of structural variation across the genus. Genome Biol. Evol. 13, 1–18 (2021). · DOI
  90. Laetsch, D. R. & Blaxter, M. L. BlobTools: interrogation of genome assemblies. F1000Research 6, 1287 (2017). · DOI
  91. Slater, G. S. C. & Birney, E. Automated generation of heuristics for biological sequence comparison. BMC Bioinforma. 6, 1–11 (2005). · DOI
  92. Ranwez, V. et al. MACSE v2: Toolkit for the alignment of coding sequences accounting for frameshifts and stop codons. Mol. Biol. Evol. 2–4, https://doi.org/10.1093/molbev/msy159 (2018). · DOI · Paper page
  93. Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2 - approximately maximum-likelihood trees for large alignments. PLoS One 5, e9490 (2010). · DOI · Paper page
  94. Paten, B. et al. Cactus: algorithms for genome multiple sequence alignment. Genome Res. 21, 1512–1528 (2011). · DOI · Paper page
  95. Revell, L. J. phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012). · DOI
  96. Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014). · DOI · Paper page
  97. 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 Genom. Bioinforma. 3, 1–11 (2021).
  98. Lomsadze, A., Ter-Hovhannisyan, V., Chernoff, Y. O. & Borodovsky, M. Gene identification in novel eukaryotic genomes by self-training algorithm. Nucleic Acids Res. 33, 6494–6506 (2005). · DOI
  99. Stanke, M. et al. AUGUSTUS: a b initio prediction of alternative transcripts. Nucleic Acids Res. 34, 435–439 (2006). · DOI · Paper page
  100. Iyer, M. K. & Chinnaiyan, A. M. RNA-Seq unleashed. Nat. Biotechnol. 29, 599–600 (2011). · DOI
  101. Haas, B. J. et al. De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis. Nat. Protoc. 8, 1494–1512 (2013). · DOI · Paper page
  102. Bateman, A. UniProt: a worldwide hub of protein knowledge. Nucleic Acids Res. https://doi.org/10.1093/nar/gky1049 (2019). · DOI · Paper page
  103. Boratyn, G. M. et al. Domain enhanced lookup time accelerated BLAST. Biol. Direct 7, 12 (2012). · DOI
  104. El-Gebali, S. et al. The Pfam protein families database in 2019. Nucleic Acids Res. https://doi.org/10.1093/nar/gky995 (2019). · DOI · Paper page
  105. Eddy, S. R. Profile hidden Markov models. Bioinformatics 14, 755–763 (1998). · DOI · Paper page
  106. Pertea, M. et al. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat. Biotechnol. 33, 290–295 (2015). · DOI
  107. Trapnell, C. et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat. Protoc. 7, 562–578 (2012). · DOI
  108. Garber, M., Grabherr, M. G., Guttman, M. & Trapnell, C. Computational methods for transcriptome annotation and quantification using RNA-seq. Nat. Methods 8, 469–477 (2011). · DOI · Paper page
  109. Trapnell, C. et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol. 28, 511–515 (2010). · DOI · Paper page
  110. Mapleson, D., Venturini, L., Kaithakottil, G. & Swarbreck, D. Efficient and accurate detection of splice junctions from RNA-seq with Portcullis. Gigascience 7, 1–11 (2018). · DOI
  111. Venturini, L., Caim, S., Kaithakottil, G. G., Mapleson, D. L. & Swarbreck, D. Leveraging multiple transcriptome assembly methods for improved gene structure annotation. Gigascience 7, 1–15 (2018). · DOI
  112. Fiddes, I. T. et al. Comparative Annotation Toolkit (CAT) - simultaneous clade and personal genome annotation. Genome Res. 231118 (2018) https://doi.org/10.1101/231118. · DOI
  113. Orme, C. D. L. The caper package: comparative analyses in phylogenetics and evolution in R. See http://caper.r-forge.r-project.org/. http://Caper.R-Forge.R-Project.Org/. 1–36 (2012). · DOI
  114. Warton, D. I., Duursma, R. A., Falster, D. S. & Taskinen, S. smatr 3- an R package for estimation and inference about allometric lines. Methods Ecol. Evol. (2012) https://doi.org/10.1111/j.2041-210X.2011.00153.x. · DOI
  115. Csürös, M. Malin: Maximum likelihood analysis of intron evolution in eukaryotes. Bioinformatics 24, 1538–1539 (2008). · DOI
  116. Derelle, R., Philippe, H. & Colbourne, J. K. Broccoli: Combining phylogenetic and network analyses for orthology assignment. Mol. Biol. Evol. 37, 3389–3396 (2020). · DOI
  117. Eddy, S. R. Accelerated profile HMM searches. PLoS Comput. Biol. 7, e1002195 (2011). · DOI · Paper page
  118. Bernardes, J., Zaverucha, G., Vaquero, C. & Carbone, A. Improvement in protein domain identification is reached by breaking consensus, with the agreement of many profiles and domain co-occurrence. PLoS Comput. Biol. 12, 1–39 (2016). · DOI
  119. Das, S. et al. CATH FunFHMMer web server: protein functional annotations using functional family assignments. Nucleic Acids Res. 43, W148–W153 (2015). · DOI
  120. Dawson, N. L. et al. CATH: An expanded resource to predict protein function through structure and sequence. Nucleic Acids Res. 45, D289–D295 (2017). · DOI
  121. Sillitoe, I. et al. CATH: comprehensive structural and functional annotations for genome sequences. Nucleic Acids Res. 43, D376–D381 (2015). · DOI
  122. Cicconardi, F., Marcatili, P., Arthofer, W., Schlick-Steiner, B. C. & Steiner, F. M. Positive diversifying selection is a pervasive adaptive force throughout the Drosophila radiation. Mol. Phylogenet. Evol. 112, 230–243 (2017). · DOI
  123. Cicconardi, F. et al. Chemosensory adaptations of the mountain fly Drosophila nigrosparsa (Insecta: Diptera) through genomics’ and structural biology’s lenses. Sci. Rep. 7, 43770 (2017). · DOI
  124. Whelan, S., Irisarri, I. & Burki, F. PREQUAL: detecting non-homologous characters in sets of unaligned homologous sequences. Bioinformatics 34, 3929–3930 (2018). · DOI
  125. Di Franco, A., Poujol, R., Baurain, D. & Philippe, H. Evaluating the usefulness of alignment filtering methods to reduce the impact of errors on evolutionary inferences. BMC Evol. Biol. 19, 1–17 (2019). · DOI
  126. Castresana, J. Selection of conserved blocks from multiple alignments for their use in phylogenetic analysis. Mol. Biol. Evol. 17, 540–552 (2000). · DOI
  127. Parker, J. et al. Genome-wide signatures of convergent evolution in echolocating mammals. Nature 502, 1–9 (2013). · DOI
  128. 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). · DOI · Paper page
  129. Kosakovsky Pond, S. L. et al. A random effects branch-site model for detecting episodic diversifying selection. Mol. Biol. Evol. 28, 3033–3043 (2011). · DOI · Paper page
  130. Smith, M. D. et al. Less is more: an adaptive branch-site random effects model for efficient detection of episodic diversifying selection. Mol. Biol. Evol. 32, 1342–1353 (2015). · DOI
  131. Kosakovsky Pond, S. L. et al. HyPhy 2.5 - a customizable platform for evolutionary hypothesis testing using phylogenies. Mol. Biol. Evol. 37, 295–299 (2020). · DOI
  132. Falcon, S. & Gentleman, R. Using GOstats to test gene lists for GO term association. Bioinformatics 23, 257–258 (2007). · DOI
  133. Klopfenstein, D. V. et al. GOATOOLS: a python library for gene ontology analyses. Sci. Rep. 8, 1–17 (2018). · DOI
  134. 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). · DOI
  135. Montojo, J., Zuberi, K., Rodriguez, H., Bader, G. D. & Morris, Q. GeneMANIA: fast gene network construction and function prediction for Cytoscape. F1000Research 3, 1–8 (2014). · DOI
  136. Vlasblom, J. et al. Novel function discovery with GeneMANIA: a new integrated resource for gene function prediction in Escherichia coli. Bioinformatics 31, 306–310 (2014). · DOI
  137. Gadagkar, S. R., Rosenberg, M. S. & Kumar, S. Inferring species phylogenies from multiple genes: Concatenated sequence tree versus consensus gene tree. J. Exp. Zool. Part B Mol. Dev. Evol. 304, 64–74 (2005). · DOI
  138. Seo, T. K., Kishino, H. & Thorne, J. L. Incorporating gene-specific variation when inferring and evaluating optimal evolutionary tree topologies from multilocus sequence data. Proc. Natl Acad. Sci. USA 102, 4436–4441 (2005). · DOI
  139. Zhang, C., Rabiee, M., Sayyari, E. & Mirarab, S. ASTRAL-III: polynomial time species tree reconstruction from partially resolved gene trees. BMC Bioinforma. 19, 15–30 (2018). · DOI · Paper page
  140. Mirarab, S. & Warnow, T. ASTRAL-II: coalescent-based species tree estimation with many hundreds of taxa and thousands of genes. Bioinformatics 31, i44–i52 (2015). · DOI
  141. Mai, U. & Mirarab, S. Completing gene trees without species trees in sub-quadratic time. Bioinformatics 38, 1532–1541 (2022). · DOI
  142. Pease, J. B., Brown, J. W., Walker, J. F., Hinchliff, C. E. & Smith, S. A. Quartet sampling distinguishes lack of support from conflicting support in the green plant tree of life. Am. J. Bot. 105, 385–403 (2018). · DOI
  143. Yang, Z. PAML 4: phylogenetic analysis by maximum likelihood. Mol. Biol. Evol. 24, 1586–1591 (2007). · DOI · Paper page
  144. Kumar, S., Stecher, G., Suleski, M. & Hedges, S. B. TimeTree: a resource for timelines, timetrees, and divergence times. Mol. Biol. Evol. 34, 1812–1819 (2017). · DOI
  145. Neafsey, D. E. et al. Extensive introgression in a malaria vector species complex revealed by phylogenomics. Science 347, 1258524 (2015). · DOI
  146. Malinsky, M., Matschiner, M. & Svardal, H. Dsuite - Fast D-statistics and related admixture evidence from VCF files. Mol. Ecol. Resour. 21, 584–595 (2021). · DOI · Paper page
  147. 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). · DOI · Paper page
  148. Larkin, M. A. et al. Clustal W and Clustal X version 2.0. Bioinformatics 23, 2947–2948 (2007). · DOI
  149. Curran, D. M., Gilleard, J. S. & Wasmuth, J. D. MIPhy: identify and quantify rapidly evolving members of large gene fam. PeerJ 2018, 1–17 (2018).
  150. Sonnhammera, E. L. & Kooninb, E. V. Orthology, paralogy and proposed classification for paralog subtypes. Trends Genet. 18, 619–620 (2002). · DOI
  151. Hubisz, M. J., Pollard, K. S. & Siepel, A. PHAST and RPHAST: phylogenetic analysis with space/time models. Brief. Bioinform. 12, 41–51 (2011). · DOI · Paper page
  152. McLean, C. Y. et al. GREAT improves functional interpretation of cis-regulatory regions. Nat. Biotechnol. 28, 495–501 (2010). · DOI · Paper page
  153. Tsirigos, K. D., Peters, C., Shu, N., Käll, L. & Elofsson, A. The TOPCONS web server for consensus prediction of membrane protein topology and signal peptides. Nucleic Acids Res. 43, W401–W407 (2015). · DOI
  154. Lu, S. et al. CDD/SPARCLE: the conserved domain database in 2020. Nucleic Acids Res. 48, D265–D268 (2020). · DOI
  155. Kosakovsky Pond, S. L. & Frost, S. D. W. Not so different after all: a comparison of methods for detecting amino acid sites under selection. Mol. Biol. Evol. 22, 1208–1222 (2005). · DOI
  156. Emanuelsson, O., Brunak, S., von Heijne, G. & Nielsen, H. Locating proteins in the cell using TargetP, SignalP and related tools. Nat. Protoc. 2, 953–971 (2007). · DOI
  157. Söding, J., Biegert, A. & Lupas, A. N. The HHpred interactive server for protein homology detection and structure prediction. Nucleic Acids Res. 33, 244–248 (2005). · DOI
  158. Grant, B. J., Rodrigues, A. P. C., ElSawy, K. M., McCammon, J. A. & Caves, L. S. D. Bio3d: an R package for the comparative analysis of protein structures. Bioinformatics 22, 2695–2696 (2006). · DOI
  159. Ruiz-Serra, V. et al. Assessing the accuracy of contact and distance predictions in CASP14. Proteins Struct. Funct. Bioinforma. 89, 1888–1900 (2021). · DOI
  160. Del Sol, A. & Carbonell, P. The modular organization of domain structures: insights into protein-protein binding. PLoS Comput. Biol. 3, 2446–2455 (2007). · DOI
  161. Yuan, Z., Bailey, T. L. & Teasdale, R. D. Prediction of protein B-factor profiles. Proteins Struct. Funct. Genet. 58, 905–912 (2005). · DOI

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Metadata

Authors
Cicconardi, F., Milanetti, E., Pinheiro de Castro, E. C., Mazo-Vargas, A., Van Belleghem, S. M., Ruggieri, A. A., Rastas, P., Hanly, J., Evans, E., Jiggins, C. D., Owen McMillan, W., Papa, R., Di Marino, D., Martin, A., & Montgomery, S. H.
ORCID
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2023-09-12
Volume
14
Issue
1
ISSN
2041-1723
DOI
10.1038/s41467-023-41412-5
PMC
PMC10497600
Cited by
81
References
11
Language
en
Licence

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

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