Zachos, J., Pagani, H., Sloan, L., Thomas, E. & Billups, K. Trends, rhythms, and aberrations in global climate 65 Ma to present. Science 292, 686–693 (2001).
Favre, A. et al. The role of the uplift of the Qinghai-Tibetan Plateau for the evolution of Tibetan biotas. Biol. Rev. Camb. Philos. Soc. 90, 236–253 (2015).
Renner, S. S. Available data point to a 4-km-high Tibetan Plateau by 40 Ma, but 100 molecular-clock papers have linked supposed recent uplift to young node ages. J. Biogeogr. 43, 1479–1487 (2016).
Xu, W. et al. Herpetological phylogeographic analyses support a Miocene focal point of Himalayan uplift and biological diversification. Natl. Sci. Rev. 8, nwaa263 (2020).
Condamine, F. L., Rolland, J., Höhna, S., Sperling, F. A. H. & Sanmartin, I. Testing the role of the Red Queen and Court Jester as drivers of the macroevolution of Apollo butterflies. Syst. Biol. 67, 940–964 (2018).
Zhao, D. N., Ren, Y. & Zhang, J. Q. Conservation and innovation: plastome evolution during rapid radiation of Rhodiola on the Qinghai-Tibetan Plateau. Mol. Phylogenet. Evol. 144, 106713 (2020).
Malhi, Y. et al. Megafauna and ecosystem function from the Pleistocene to the Anthropocene. Proc. Natl. Acad. Sci. USA 113, 838–846 (2016).
Galetti, M. et al. Ecological and evolutionary legacy of megafauna extinctions. Biol. Rev. 93, 845–862 (2018).
Stuart, A. J., Kosintsev, P. A., Higham, T. F. G. & Lister, A. M. Pleistocene to Holocene extinction dynamics in giant deer and woolly mammoth. Nature 431, 684–689 (2004).
Davis, M., Faurby, S. & Svenning, J. C. Mammal diversity will take millions of years to recover from the current biodiversity crisis. Proc. Natl. Acad. Sci. USA 115, 11262–11267 (2018).
Wang, X. Y. et al. Out of Tibet: Genomic perspectives on the evolutionary history of extant pikas. Mol. Biol. Evol. 37, 1577–1592 (2020).
Ge, D. et al. Demographic history and genomic response to environmental changes in a rapid radiation of wild rats. Mol. Biol. Evol. 38, 1905–1923 (2021).
Zhao, Y. et al. Phylogeny and biogeographic history of Parnassius butterflies (Papilionidae: Parnassiinae) reveal their origin and deep diversification in West China. Insects 13, 406 (2022).
Liu, G. et al. Genome size variation in butterflies (Insecta, Lepidotera, Papilionoidea): a thorough phylogenetic comparison. Syst. Entomol. 45, 571–582 (2020).
He, J. W. et al. High-quality reference genomes of swallowtail butterflies provide insights into their coloration evolution. Zool. Res. 43, 367–379 (2022).
Podsiadlowski, L., Tunström, K., Espeland, M. & Wheat, C. W. The genome assembly and annotation of the Apollo butterfly Parnassius apollo, a flagship species for conservation biology. Genome Biol. Evol. 13, evab122 (2021).
Li, X. et al. Outbred genome sequencing and CRISPR/Cas9 gene editing in butterflies. Nat. Commun. 6, 8212 (2015).
Tao, R. et al. Spatiotemporal differentiation of alpine butterfly Parnassius glacialis (Papilionidae: Parnassiinae) in China: Evidence from mitochondrial DNA and nuclear single nucleotide polymorphisms. Genes 11, 188 (2020).
Hao, X., Mao, Z., Ren, H. & Rao, R. Analysis of geometric morphological of vein of Parnassius glacialis in different geographic populations. J. Anhui Agric. Sci 34, 84–88 (2017).
Harrison, J. F. & Lighton, J. R. B. Oxygen-sensitive flight metabolism in the dragonfly Erythemis simplicicollis. J. Exp. Biol. 201, 1739–1744 (1998).
Klok, C. J. & Harrison, J. F. Atmospheric hypoxia limits selection for large body size in insects. PLoS ONE 4, e3876 (2009).
Sanabria-Urbán, S. et al. Body size adaptations to altitudinal climatic variation in neotropical grasshoppers of the genus Sphenarium (Orthoptera: Pyrgomorphidae). PLoS ONE 10, e0145248 (2015).
Harrison, J. F., Kaiser, A. & VandenBrooks, J. M. Atmospheric oxygen level and the evolution of insect body size. Proc. R. Soc. B Biol. Sci. 277, 1937–1946 (2010).
Lu, S. et al. Chromosomal-level reference genome of Chinese peacock butterfly (Papilio bianor) based on third-generation DNA sequencing and Hi-C analysis. Gigascience 8, giz128 (2019).
Wicker, T. et al. A unified classification system for eukaryotic transposable elements. Nat. Rev. Genet. 8, 973–982 (2007).
Devos, K. M., Brown, J. K. & Bennetzen, J. L. Genome size reduction through illegitimate recombination counteracts genome expansion in Arabidopsis. Genome Res. 12, 1075–1079 (2002).
Artero-Castro, A. et al. Disruption of the ribosomal P complex leads to stress-induced autophagy. Autophagy 11, 1499–1519 (2015).
Yang et al. Senescent cells differentially translate senescence-related mRNAs Via ribosome heterogeneity. J. Gerontol. A Biol. Sci. Med. Sci. 74, 1015–1024 (2019).
Clark, P. U. et al. The last glacial maximum. Science 325, 710–714 (2009).
Candas, M., Sohal, R. S., Radyuk, S. N., Klichko, V. I. & Orr, W. C. Molecular organization of the glutathione reductase gene in Drosophila melanogaster. Arch. Biochem. Biophys. 339, 323–334 (1997).
Zhang, L., Yue, T. & Jiang, J. Hippo signaling pathway and organ size control. Fly 3, 68–73 (2009).
Myllymäki, H., Valanne, S. & Rämet, M. The Drosophila imd signaling pathway. J. Immunol. 192, 3455–3462 (2014).
Feschotte, C. & Pritham, E. J. DNA Transposons and the evolution of eukaryotic genomes. Annu. Rev. Genet. 41, 331–368 (2007).
Chalopin, D., Naville, M., Plard, F., Galiana, D. & Volff, J. N. Comparative analysis of transposable elements highlights mobilome diversity and evolution in vertebrates. Genome Biol. Evol. 7, 567–580 (2015).
Platt, R. N., Vandewege, M. W. & Ray, D. A. Mammalian transposable elements and their impacts on genome evolution. Chromosome Res. 26, 25–43 (2018).
Gilbert, C., Peccoud, J. & Cordaux, R. Transposable elements and the evolution of insects. Annu. Rev. Entomol. 66, 355–372 (2021).
Oliver, K. R., McComb, J. A. & Greene, W. K. Transposable elements: powerful contributors to angiosperm evolution and diversity. Genome Biol. Evol. 5, 1886–1901 (2013).
Ray, D. A. et al. Simultaneous TE analysis of 19 Heliconiine butterflies yields novel insights into rapid TE-based genome diversification and multiple SINE births and deaths. Genome Biol. Evol. 11, 2162–2177 (2019).
Lanciano, S. & Mirouze, M. Transposable elements: all mobile, all different, some stress responsive, some adaptive? Curr. Opin. Genet. Dev. 49, 106–114 (2018).
Srikant, T. & Drost, H. G. How stress facilitates phenotypic innovation through epigenetic Diversity. Front. Plant Sci. 11, 606800 (2021).
Wong, W. Y. et al. Expansion of a single transposable element family is associated with genome-size increase and radiation in the genus Hydra. Proc. Natl. Acad. Sci. USA 116, 22915–22917 (2019).
Zhang, Z., Harrison, P. & Gerstein, M. Identification and analysis of over 2000 ribosomal protein pseudogenes in the human genome. Genome Res. 12, 1466–1482 (2002).
Tan, S. et al. LTR-mediated retroposition as a mechanism of RNA-based duplication in metazoans. Genome Res. 26, 1663–1675 (2016).
Troskie, R. L., Faulkner, G. J. & Cheetham, S. W. Processed pseudogenes: a substrate for evolutionary innovation: Retrotransposition contributes to genome evolution by propagating pseudogene sequences with rich regulatory potential throughout the genome. BioEssays 43, e2100186 (2021).
Cheng, Y. et al. Parallel genomic responses to historical climate change and high elevation in East Asian songbirds. Proc. Natl. Acad. Sci. USA 118, e2023918118 (2021).
Trense, D., Hoffmann, A. A. & Fischer, K. Large- and small-scale geographic structures affecting genetic patterns across populations of an Alpine butterfly. Ecol. Evol. 11, 14697–14714 (2021).
Nachman, M. W. & Payseur, B. A. Recombination rate variation and speciation: theoretical predictions and empirical results from rabbits and mice. Philos. Trans. R. Soc. Lond. B Biol. Sci. 367, 409–421 (2012).
Kent, T. V., Uzunović, J. & Wright, S. I. Coevolution between transposable elements and recombination. Philos. Trans. R. Soc. Lond. B Biol. Sci. 372, 20160458 (2017).
Landis, G., Shen, J. & Tower, J. Gene expression changes in response to aging compared to heat stress, oxidative stress and ionizing radiation in Drosophila melanogaster. Aging 4, 768–789 (2012).
Ortiz, J. G., Opoka, R., Kane, D. & Cartwright, I. L. Investigating arsenic susceptibility from a genetic perspective in Drosophila reveals a key role for glutathione synthetase. Toxicol. Sci. 107, 416–426 (2009).
Low, W. Y. et al. Molecular evolution of glutathione S-transferases in the genus Drosophila. Genetics 177, 1363–1375 (2007).
He, B. et al. Phylogenomics reveal extensive phylogenetic discordance due to incomplete lineage sorting following the rapid radiation of alpine butterflies (Papilionidae: Parnassius). Syst. Entomol. doi.org/10.1111/syen.12592 (2023).
Marçais, G. & Kingsford, C. A fast, lock-free approach for efficient parallel counting of occurrences of k-mers. Bioinformatics 27, 764–770 (2011).
Liu, B. et al. Estimation of genomic characteristics by analyzing k-mer frequency in de novo genome projects. Quant. Biol. 35, 62–67 (2013).
Cheng, H., Concepcion, G. T., Feng, X., Zhang, H. & Li, H. Haplotype-resolved de novo assembly with phased assembly graphs. Nat. Methods. 18, 170–175 (2021).
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).
Zhang, X., Zhang, S., Zhao, Q., Ming, R. & Tang, H. Assembly of allele-aware, chromosomal-scale autopolyploid genomes based on Hi-C data. Nat. Plants 5, 833–845 (2019).
Seppey, M., Manni, M. & Zdobnov, E. M. BUSCO: assessing genome assembly and annotation completeness. Methods Mol. Biol. 1962, 227–245 (2019).
Wang, Y. et al. MCScanX: a toolkit for detection and evolutionary analysis of gene synteny and collinearity. Nucleic Acids Res. 40, e49 (2012).
Flynn, J. M. et al. RepeatModeler2 for automated genomic discovery of transposable element families. Proc. Natl. Acad. Sci. USA 117, 9451–9457 (2020).
Zhao, X. & Hao, W. LTR_FINDER: an efficient tool for the prediction of full-length LTR retrotransposons. Nucleic Acids Res. 35, W265–W268 (2007).
Ellinghaus, D., Kurtz, S. & Willhoeft, U. LTRharvest, an efficient and flexible software for de novo detection of LTR retrotransposons. BMC Bioinforma. 9, 18 (2008).
Price, A. L., Jones, N. C. & Pevzner, P. A. De novo identification of repeat families in large genomes. Bioinformatics 21 Suppl 1, i351–i358 (2005).
Tarailo-Graovac, M. & Chen, N. Using RepeatMasker to identify repetitive elements in genomic sequences. Curr. Protoc. Bioinforma. Chapter 4, Unit 4.10 (2009).
Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: Improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).
Rice, P., Longden, I. & Bleasby, A. EMBOSS: the European molecular biology open software suite. Trends Genet. 16, 276–277 (2000).
Parra, G., Blanco, E. & Guigó, R. GeneId in Drosophila. Genome Res. 10, 511–515 (2000).
Burge, C. & Karlin, S. Prediction of complete gene structures in human genomic DNA. J. Mol. Biol. 268, 78–94 (1997).
Majoros, W. H., Pertea, M. & Salzberg, S. L. TigrScan and GlimmerHMM: two open source ab initio eukaryotic gene-finders. Bioinformatics 20, 2878–2879 (2004).
Birney, E., Clamp, M. & Durbin, R. GeneWise and genomewise. Genome Res 14, 988–995 (2004).
Stanke, M. & Morgenstern, B. AUGUSTUS: a web server for gene prediction in eukaryotes that allows user-defined constraints. Nucleic Acids Res. 33, W465–W467 (2005).
Keilwagen, J., Hartung, F. & Grau, J. GeMoMa: homology-based gene prediction utilizing intron position conservation and RNA-seq data. Methods Mol. Biol. 1962, 161–177 (2019).
Su, C. et al. Diapause-linked gene expression pattern and related candidate duplicated genes of the mountain butterfly Parnassius glacialis (Lepidoptera: Papilionidae) revealed by comprehensive transcriptome profiling. Int. J. Mol. Sci. 24, 5577 (2023).
Kim, D., Langmead, B. & Salzberg, S. L. HISAT: a fast spliced aligner with low memory requirements. Nat. Methods. 12, 357–360 (2015).
Pertea, M. et al. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat. Biotechnol. 33, 290–295 (2015).
Tang, S., Lomsadze, A. & Borodovsky, M. Identification of protein coding regions in RNA transcripts. Nucleic Acids Res. 43, e78 (2015).
Haas, B. J. et al. Automated eukaryotic gene structure annotation using EVidenceModeler and the program to assemble spliced alignments. Genome Biol. 9, R7 (2008).
Ye, J. et al. WEGO: A web tool for plotting GO annotations. Nucleic Acids Res. 34, W293–W297 (2006).
Kanehisa, M., Sato, Y., Kawashima, M., Furumichi, M. & Tanabe, M. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 44, D457–D462 (2016).
Bateman, A. et al. UniProt: the universal protein knowledgebase in 2021. Nucleic Acids Res. 49, D480–D489 (2021).
Mistry, J. et al. Pfam: The protein families database in 2021. Nucleic Acids Res. 49, D412–D419 (2021).
Zhan, S., Merlin, C., Boore, J. L. & Reppert, S. M. The monarch butterfly genome yields insights into long-distance migration. Cell 147, 1171–1185 (2011).
Xia, Q. et al. The genome of a lepidopteran model insect, the silkworm Bombyx mori. Insect Biochem. Mol. Biol. 38, 1036–1045 (2008).
Emms, D. M. & Kelly, S. OrthoFinder: phylogenetic orthology inference for comparative genomics. Genome Biol. 20, 238 (2019).
Capella-Gutiérrez, S., Silla-Martínez, J. M. & Gabaldón, T. trimAl: A tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 25, 1972–1973 (2009).
Stamatakis, A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313 (2014).
Kawahara, A. Y. et al. Phylogenomics reveals the evolutionary timing and pattern of butterflies and moths. Proc. Natl. Acad. Sci. USA 116, 22657–22663 (2019).
Yang, Z. PAML 4: phylogenetic analysis by maximum likelihood. Mol. Biol. Evol. 24, 1586–1591 (2007).
De Bie, T., Cristianini, N., Demuth, J. P. & Hahn, M. W. CAFE: a computational tool for the study of gene family evolution. Bioinformatics 22, 1269–1271 (2006).
Rambaut, A. FigTree v1.4.4, A graphical viewer of phylogenetic trees. Available from: github.com/rambaut/figtree/releases (2014).
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).
Slater, G. S. C. & Birney, E. Automated generation of heuristics for biological sequence comparison. BMC Bioinforma. 6, 31 (2005).
Wang, D. P., Wan, H. L., Zhang, S. & Yu, J. Gamma-MYN: a new algorithm for estimating Ka and Ks with consideration of variable substitution rates. Biol. Direct 4, 20 (2009).
Drummond, A. J., Suchard, M. A., Xie, D. & Rambaut, A. Bayesian phylogenetics with BEAUti and the BEAST 1.7. Mol. Biol. Evol. 29, 1969–1973 (2012).
Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).
Kent, W. J. BLAT—the BLAST-like alignment tool. Genome Res. 12, 656–664 (2002).
Bowen, N. J. & McDonald, J. F. Drosophila euchromatic LTR retrotransposons are much younger than the host species in which they reside. Genome Res. 11, 1527–1540 (2001).
Li, H. et al. The Sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
Li, H. & Durbin, R. Inference of human population history from individual whole-genome sequences. Nature 475, 493–496 (2011).
Poplin, R. et al. A universal SNP and small-indel variant caller using deep neural networks. Nat. Biotechnol. 36, 983 (2018).
Hardvard University. PLINK: Whole genome data analysis toolset. Am. J. Hum. Genet. 81, 559–575 (2017).
Alexander, D. H., Novembre, J. & Lange, K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 19, 1655–1664 (2009).
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).
Pickrell, J. K. & Pritchard, J. K. Inference of population splits and mixtures from genome-wide allele frequency data. PLoS Genet. 8, e1002967 (2012).
Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011).
Tian, S. et al. Genomic analyses reveal genetic adaptations to tropical climates in chickens. iScience 23, 101644 (2020).
Szpiech, Z. A. & Hernandez, R. D. selscan: an efficient multithreaded program to perform EHH-based scans for positive selection. Mol. Biol. Evol. 31, 2824–2827 (2014).
Quinlan, A. R. BEDTools: the Swiss-army tool for genome feature analysis. Curr. Protoc. Bioinforma. 47, 11.12.1–11.12.34 (2014).
Chen, X. et al. Manta: rapid detection of structural variants and indels for germline and cancer sequencing applications. Bioinformatics 32, 1220–1222 (2016).
Layer, R. M., Chiang, C., Quinlan, A. R. & Hall, I. M. LUMPY: a probabilistic framework for structural variant discovery. Genome Biol. 15, R84 (2014).
Mérot, C. et al. Genome assembly, structural variants, and genetic differentiation between lake whitefish young species pairs (Coregonus sp.) with long and short reads. Mol. Ecol. 32, 1458–1477 (2023).
Kirsche, M. et al. Jasmine and Iris: population-scale structural variant comparison and analysis. Nat. Methods 20, 408–417 (2023).
Chan, A. H., Jenkins, P. A. & Song, Y. S. Genome-wide fine-scale recombination rate variation in Drosophila melanogaster. PLoS Genet. 8, e1003090 (2012).
Martin, S. H., Davey, J. W., Salazar, C. & Jiggins, C. D. Recombination rate variation shapes barriers to introgression across butterfly genomes. PLoS Biol. 17, e2006288 (2019).
Montejo-Kovacevich, G. et al. Repeated genetic adaptation to altitude in two tropical butterflies. Nature Commun. 13, 4676 (2022).
Read more here: Source link