Qin, Y.-j. et al. Population structure of a global agricultural invasive pest, Bactrocera dorsalis (Diptera: Tephritidae). Evol. Appl. 11, 1990–2003 (2018).
Google Scholar
Christenson, L. D. & Foote, R. H. Biology of fruit flies. Annu. Rev. Entomol. 5, 171–192 (1960).
Clarke, A. R. et al. Invasive phytophagous pests arising through a recent tropical evolutionary radiation: the Bactrocera dorsalis complex of fruit flies. Annu. Rev. Entomol. 50, 293–319 (2005).
Google Scholar
Culliney, T. W. The aliens have landed: invasive species threaten Hawaii agriculture. Agric. Hawaii 3, 6–9 (2002).
Cantrell, B., Chadwick, B. & Cahill, A. Fruit Fly Fighters: Eradication of the Papaya Fruit Fly (CSIRO, Collingwood, 2002).
Ekesi, S., De Meyer, M., Mohamed, S. A., Virgilio, M. & Borgemeister, C. Taxonomy, ecology, and management of native and exotic fruit fly species in Africa. Annu. Rev. Entomol. 61, 219–238 (2016).
Google Scholar
Duyck, P. F., David, P. & Quilici, S. A review of relationships between interspecific competition and invasions in fruit flies (Diptera: Tephritidae). Ecol. Entomol. 29, 511–520 (2004).
Liu, H., Zhang, C., Hou, B. H., Ou-Yang, G. C. & Ma, J. Interspecific competition between Ceratitis capitata and two Bactrocera spp. (Diptera: Tephritidae) evaluated via adult behavioral interference under laboratory conditions. J. Econ. Entomol. 110, 1145–1155 (2017).
Google Scholar
Li, F. et al. Insect genomes: progress and challenges. Insect Mol. Biol. 28, 739–758 (2019).
Google Scholar
Schutze, M. K. et al. Synonymization of key pest species within the Bactrocera dorsalis complex (Diptera: Tephritidae): taxonomic changes based on a review of 20 years of integrative morphological, molecular, cytogenetic, behavioural, and chemoecological data. Syst. Entomol. 40, 456–471 (2015).
Dudchenko, O. et al. De novo assembly of the Aedes aegypti genome using Hi-C yields chromosome-length scaffolds. Science 356, 92–95 (2017).
Google Scholar
Wu, N. et al. Fall webworm genomes yield insights into rapid adaptation of invasive species. Nat. Ecol. Evol. 3, 105–115 (2019).
Google Scholar
Papanicolaou, A. et al. The whole genome sequence of the Mediterranean fruit fly, Ceratitis capitata (Wiedemann), reveals insights into the biology and adaptive evolution of a highly invasive pest species. Genome Biol. 17, 192 (2016).
Google Scholar
Sim, S. B. & Geib, S. M. A chromosome-scale assembly of the Bactrocera cucurbitae genome provides insight to the genetic basis of white pupae. G3 (Bethesda) 7, 1927–1940 (2017).
Google Scholar
Parra, G., Bradnam, K. & Korf, I. CEGMA: a pipeline to accurately annotate core genes in eukaryotic genomes. Bioinformatics 23, 1061–1067 (2007).
Google Scholar
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).
Ashburner, M. et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25, 25–29 (2000).
Google Scholar
Kanehisa, M., Goto, S., Sato, Y., Furumichi, M. & Tanabe, M. KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. 40, 109–114 (2012).
Ting, C. T. et al. Gene duplication and speciation in Drosophila: evidence from the Odysseus locus. Proc. Natl Acad. Sci. USA 101, 12232–12235 (2004).
Google Scholar
Yuan, Y. W. & Wessler, S. R. The catalytic domain of all eukaryotic cut-and-paste transposase superfamilies. Proc. Natl Acad. Sci. USA 108, 7884–7889 (2011).
Google Scholar
Rappoport, N. & Linial, M. Trends in genome dynamics among major orders of insects revealed through variations in protein families. BMC Genomics 16, 583 (2015).
Google Scholar
Sackton, T. B. et al. Dynamic evolution of the innate immune system in Drosophila. Nat. Genet. 39, 1461–1468 (2007).
Google Scholar
Stephen, B. H. & David, L. H. Key evolutionary innovations and their ecological mechanisms. Hist. Biol. 10, 151–173 (1995).
Yu, G. C., Wang, L. G., Han, Y. Y. & He, Q. Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284–287 (2012).
Google Scholar
Lindquist, S. The heat-shock response. Annu. Rev. Biochem. 55, 1151–1191 (1986).
Google Scholar
Parsell, D. A. & Lindquist, S. The function of heat-shock proteins in stress tolerance-degradation and reactivation of damaged proteins. Annu. Rev. Genet. 27, 437–496 (1993).
Google Scholar
Feder, M. E. & Hofmann, G. E. Heat-shock proteins, molecular chaperones, and the stress response. Annu. Rev. Physiol. 61, 243–282 (1999).
Google Scholar
Iwama, G. K., Thomas, P. T., Forsyth, R. H. B. & Vijayan, M. M. Heat shock protein expression in fish. Rev. Fish. Biol. Fish. 8, 35–56 (1998).
Azad, P., Ryu, J. & Haddad, G. G. Distinct role of Hsp70 in Drosophila hemocytes during severe hypoxia. Free Radic. Biol. Med. 51, 530–538 (2011).
Google Scholar
Zhao, P. et al. Genome-wide analysis of the potato hsp20 gene family: identification, genomic organization and expression profiles in response to heat stress. BMC Genomics 19, 61 (2018).
Google Scholar
Weinstein, D. J. et al. The genome of a subterrestrial nematode reveals adaptations to heat. Nat. Commun. 10, 5268 (2019).
Google Scholar
Gu, X. et al. A transcriptional and functional analysis of heat hardening in two invasive fruit fly species, Bactrocera dorsalis, and Bactrocera correcta. Evol. Appl. 12, 1147–1163 (2019).
Google Scholar
Sorensen, J. G., Dahlgaard, J. & Loeschcke, V. Genetic variation in thermal tolerance among natural populations of drosophila buzzatii: down regulation of hsp70 expression and variation in heat stress resistance traits. Funct. Ecol. 15, 289–296 (2001).
Terblanche, J. S. et al. Ecologically relevant measures of tolerance to potentially lethal temperatures. J. Exp. Biol. 214, 3713–3725 (2011).
Google Scholar
Raza, M. F. et al. Gut microbiota promotes host resistance to low-temperature stress by stimulating its arginine and proline metabolism pathway in adult Bactrocera dorsalis. PLoS Pathog. 16, e1008441 (2020).
Google Scholar
Trempolec, N., Dave-Coll, N. & Nebreda, A. R. Snapshot: p38 MAPK signaling. Cell 152, 656–656.e1 (2013).
Google Scholar
Tatar, M. et al. A mutant Drosophila insulin receptor homolog that extends life-span and impairs neuroendocrine function. Science 292, 107–110 (2001).
Google Scholar
Vrailas-Mortimer, A. et al. A muscle-specific p38 MAPK/Mef2/MnSOD pathway regulates stress, motor function, and life span in Drosophila. Dev. Cell 21, 783–795 (2011).
Google Scholar
Li, F. F., Xia, J., Li, J. M., Liu, J. M. & Wang, X. W. P38 MAPK is a component of the signal transduction pathway triggering cold stress response in the med cryptic species of Bemisia tabaci. J. Integr. Agr. 11, 303–311 (2012).
Google Scholar
Xiao, X. P. et al. A Mesh-Duox pathway regulates homeostasis in the insect gut. Nat. Microbiol. 2, 17020 (2017).
Google Scholar
Wan, F. et al. A chromosome-level genome assembly of Cydia pomonella provides insights into chemical ecology and insecticide resistance. Nat. Commun. 10, 4237–4237 (2019).
Google Scholar
Drew, R. & Yuval, B. Fruit Flies (Tephritidae): Phylogeny and Evolution of Behavior (eds Aluja, M. & Norrbom, A.) 731−749 (CRC Press, 2000).
Dahanukar, A., Hallem, E. A. & Carlson, J. R. Insect chemoreception. Curr. Opin. Neurobiol. 15, 423–430 (2005).
Google Scholar
Bargmann, C. I. Comparative chemosensation from receptors to ecology. Nature 444, 295 (2006).
Google Scholar
Benton, R. Multigene family evolution: perspectives from insect chemoreceptors. Trends Ecol. Evol. 30, 590–600 (2015).
Google Scholar
Miyazaki, H. et al. Functional characterization of olfactory receptors in the Oriental fruit fly Bactrocera dorsalis that respond to plant volatiles. Insect Biochem. Mol. Biol. 101, 32–46 (2018).
Google Scholar
Ono, H. et al. Functional characterization of olfactory receptors in three Dacini fruit flies (Diptera: Tephritidae) that respond to 1-nonanol analogs as components in the rectal glands. Comp. Biochem. Physiol. B: Biochem. Mol. Biol. 239, 110346 (2020).
Harris, R. M. & Hofmann, H. A. Seeing is believing: dynamic evolution of gene families. Proc. Natl Acad. Sci. USA 112, 1252–1253 (2015).
Google Scholar
Nei, M., Niimura, Y. & Nozawa, M. The evolution of animal chemosensory receptor gene repertoires: roles of chance and necessity. Nat. Rev. Genet. 9, 951−963 (2008).
Arguello, J. R. et al. Extensive local adaptation within the chemosensory system following Drosophila melanogaster’s global expansion. Nat. Commun. 7, 11855 (2016).
Li, S. et al. The genomic and functional landscapes of developmental plasticity in the American cockroach. Nat. Commun. 9, 1008 (2018).
Google Scholar
Vontas, J. et al. Insecticide resistance in Tephritid flies. Pestic. Biochem. Physiol. 100, 199–205 (2011).
Google Scholar
Bergé, J. B., Feyereisen, R. & Amichot, M. Cytochrome P450 monooxygenases and insecticide resistance in insects. Philos. Trans. R. Soc. Lond. B: Biol. Sci. 353, 1701–1705 (1998).
Scott, J. G. Cytochromes P450 and insecticide resistance. Insect Biochem. Mol. Biol. 29, 757–777 (1999).
Google Scholar
Rane, R. V. et al. Detoxifying enzyme complements and host use phenotypes in 160 insect species. Curr. Opin. Insect Sci. 31, 131–138 (2019).
Google Scholar
Pendleton, M. et al. Assembly and diploid architecture of an individual human genome via single-molecule technologies. Nat. Methods 12, 780–786 (2015).
Google Scholar
Belton, J. M. et al. Hi-C: a comprehensive technique to capture the conformation of genomes. Methods 58, 268–276 (2012).
Google Scholar
Burton, J. N., Liachko, I., Dunham, M. J. & Shendure, J. Species-level deconvolution of metagenome assemblies with Hi-C-based contact probability maps. G3-Genes Genom. Genet. 4, 1339–1346 (2014).
Burton, J. N. et al. Chromosome-scale scaffolding of de novo genome assemblies based on chromatin interactions. Nat. Biotechnol. 31, 1119–1125 (2013).
Google Scholar
Servant, N. et al. HiC-Pro: an optimized and flexible pipeline for Hi-C data processing. Genome Biol. 16, 1–11 (2015).
Koren, S. et al. Canu: scalable and accurate long-read assembly via adaptive k-mer weighting and repeat separation. Genome Res. 27, 722–736 (2017).
Google Scholar
Walker, B. J. et al. Pilon: an integrated tool for comprehensive microbial variant detection and genome assembly improvement. PLoS One 9, e112963 (2014).
Google Scholar
Chakraborty, M., Baldwin-Brown, J. G., Long, A. D. & Emerson, J. J. Contiguous and accurate de novo assembly of metazoan genomes with modest long read coverage. Nucleic Acids Res. 44, e147 (2016).
Google Scholar
Chin, C. S. et al. Phased diploid genome assembly with single-molecule real-time sequencing. Nat. Methods 13, 1050–1054 (2016).
Google Scholar
Xiao, C. L. et al. MECAT: fast mapping, error correction, and de novo assembly for single-molecule sequencing reads. Nat. Methods 14, 1072–1074 (2017).
Google Scholar
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows−Wheeler transform. Bioinformatics 25, 1754–1760 (2009).
Google Scholar
Worley, K. C. et al. Improving genomes using long reads and PBJelly 2. In International Plant & Animal Genome Conference XXI (2014).
Krzywinski, M. et al. Circos: An information aesthetic for comparative genomics. Genome Res. 19, 1639–1645 (2009).
Google Scholar
Burge, C. & Karlin, S. Prediction of complete gene structures in human genomic DNA. J. Mol. Biol. 268, 78–94 (1997).
Google Scholar
Stanke, M. & Waack, S. Gene prediction with a hidden Markov model and a new intron submodel. Bioinformatics 19, ii215–ii225 (2003).
Google Scholar
Korf, I. Gene finding in novel genomes. BMC Bioinform. 5, 59 (2004).
Majoros, W. H., Pertea, M. & Salzberg, S. L. TigrScan and GlimmerHMM: two open source ab initio eukaryotic gene-finders. Bioinformatics 20, 2878–2879 (2004).
Google Scholar
Blanco, E., Parra, G. & Guigó, R. Using geneid to identify genes. Curr. Protoc. Bioinform. 18, 4.3.1–4.3.28 (2007).
Keilwagen, J. et al. Using intron position conservation for homology-based gene prediction. Nucleic Acids Res. 44, e89–e89 (2016).
Google Scholar
Campbell, M. A., Haas, B. J., Hamilton, J. P., Mount, S. M. & Buell, C. R. Comprehensive analysis of alternative splicing in rice and comparative analyses with Arabidopsis. BMC Genomics 7, 327 (2006).
Google Scholar
Haas, B. J. et al. Automated eukaryotic gene structure annotation using EVidenceModeler and the Program to Assemble Spliced Alignments. Genome Biol. 9, R7 (2008).
Google Scholar
Lowe, T. M. & Eddy, S. R. tRNAscan-SE: a program for improved detection of transfer RNA genes in genomic sequence. Nucleic Acids Res. 25, 955–964 (1997).
Google Scholar
Griffiths-Jones, S. et al. Rfam: annotating non-coding RNAs in complete genomes. Nucleic Acids Res. 33, D121–D124 (2005).
Google Scholar
Griffiths-Jones, S., Grocock, R. J., Van Dongen, S., Bateman, A. & Enright, A. J. miRBase: microRNA sequences, targets, and gene nomenclature. Nucleic Acids Res. 34, D140–D144 (2006).
Google Scholar
Nawrocki, E. P. & Eddy, S. R. Infernal 1.1: 100-fold faster RNA homology searches. Bioinformatics 29, 2933–2935 (2013).
Google Scholar
Edgar, R. C. & Myers, E. W. PILER: Identification and classification of genomic repeats. Bioinformatics 21, i152–i158 (2005).
Google Scholar
Price, A. L., Jones, N. C. & Pevzner, P. A. De novo identification of repeat families in large genomes. Bioinformatics 21, i351–i358 (2005).
Google Scholar
Xu, Z. & Wang, H. LTR_FINDER: an efficient tool for the prediction of full-length LTR retrotransposons. Nucleic Acids Res. 35, W265–W268 (2007).
Google Scholar
Han, Y. & Wessler, S. R. MITE-Hunter: a program for discovering miniature inverted-repeat transposable elements from genomic sequences. Nucleic Acids Res. 38, e199 (2010).
Google Scholar
Jurka, J. et al. Repbase Update, a database of eukaryotic repetitive elements. Cytogenet Genome Res. 110, 462–467 (2005).
Google Scholar
Wicker, T. et al. A unified classification system for eukaryotic transposable elements. Nat. Rev. Genet. 8, 973–982 (2007).
Google Scholar
Tarailo-Graovac, M. & Chen, N. S. Using RepeatMasker to identify repetitive elements in genomic sequences. Curr. Protoc. Bioinform. 25, 4.10.1–4.10.14 (2009).
Wang, Y. et al. MCScanX: a toolkit for detection and evolutionary analysis of gene synteny and collinearity. Nucleic Acids Res. 40, e49 (2012).
Google Scholar
Birney, E., Clamp, M. & Durbin, R. GeneWise and Genomewise. Genome Res. 14, 988–995 (2004).
Google Scholar
She, R., Chu, J. S. C., Wang, K., Pei, J. & Chen, N. S. GenBlastA: enabling BLAST to identify homologous gene sequences. Genome Res. 19, 143–149 (2009).
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
Kanehisa, M. & Goto, S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27–30 (2000).
Google Scholar
Tatusov, R. L. et al. The COG database: new developments in phylogenetic classification of proteins from complete genomes. Nucleic Acids Res. 29, 22–28 (2001).
Google Scholar
Boeckmann, B. et al. The SWISS-PROT protein KnowledgeBase and its supplement TrEMBL in 2003. Nucleic Acids Res. 31, 365–370 (2003).
Google Scholar
Marchler-Bauer, A. et al. CDD: a Conserved Domain Database for the functional annotation of proteins. Nucleic Acids Res. 39, D225–D229 (2011).
Google Scholar
Conesa, A. et al. Blast2GO: a universal tool for annotation, visualization, and analysis in functional genomics research. Bioinformatics 21, 3674–3676 (2005).
Google Scholar
Dimmer, E. C. et al. The UniProt-GO annotation database in 2011. Nucleic Acids Res. 40, D565–D570 (2012).
Google Scholar
Bairoch, A. PROSITE-a dictionary of sites and patterns in proteins. Nucleic Acids Res. 19, 2241–2245 (1991).
Google Scholar
Attwood, T. K. & Beck, M. E. Prints-a protein motif fingerprint database. Protein Eng. Des. Sel. 7, 841–848 (1994).
Google Scholar
Zdobnov, E. M. & Apweiler, R. InterProScan-an integration platform for the signature-recognition methods in InterPro. Bioinformatics 17, 847–848 (2001).
Google Scholar
Gough, J. & Chothia, C. SUPERFAMILY: HMMs representing all proteins of known structure. SCOP sequence searches, alignments, and genome assignments. Nucleic Acids Res. 30, 268–272 (2002).
Google Scholar
Haft, D. H., Selengut, J. D. & White, O. The TIGRFAMs database of protein families. Nucleic Acids Res. 31, 371–373 (2003).
Google Scholar
Thomas, P. D. et al. PANTHER: a browsable database of gene products organized by biological function, using curated protein family and subfamily classification. Nucleic Acids Res. 31, 334–341 (2003).
Google Scholar
Letunic, I. et al. SMART 4.0: towards genomic data integration. Nucleic Acids Res. 32, D142–D144 (2004).
Google Scholar
Wu, C. H. et al. PIRSF: family classification system at the Protein Information Resource. Nucleic Acids Res. 32, D112–D114 (2004).
Google Scholar
Bru, C. et al. The ProDom database of protein domain families: more emphasis on 3D. Nucleic Acids Res. 33, D212–D215 (2005).
Google Scholar
Finn, R. D. et al. Pfam: clans, web tools, and services. Nucleic Acids Res. 34, D247–D251 (2006).
Google Scholar
Lima, T. et al. HAMAP: a database of completely sequenced microbial proteome sets and manually curated microbial protein families in UniProtKB/Swiss-Prot. Nucleic Acids Res. 37, D471–D478 (2009).
Google Scholar
Lees, J. et al. Gene3D: a domain-based resource for comparative genomics, functional annotation, and protein network analysis. Nucleic Acids Res. 40, D465–D471 (2012).
Google Scholar
Emms, D. M. & Kelly, S. OrthoFinder: phylogenetic orthology inference for comparative genomics. Genome Biol. 20, 238 (2019).
Google Scholar
Mi, H. Y., Muruganujan, A., Ebert, D., Huang, X. S. & Thomas, P. D. PANTHER version 14: more genomes, a new PANTHER GO-slim, and improvements in enrichment analysis tools. Nucleic Acids Res. 47, D419–D426 (2019).
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
Puttick, M. N. MCMCtreeR: functions to prepare MCMCtree analyses and visualize posterior ages on trees. Bioinformatics 35, 5321–5322 (2019).
Google Scholar
Yang, Z. H. PAML: a program package for phylogenetic analysis by maximum likelihood. Bioinformatics 13, 555–556 (1997).
Google Scholar
Han, M. V., Thomas, G. W. C., Lugo-Martinez, J. & Hahn, M. W. Estimating gene gain and loss rates in the presence of error in genome assembly and annotation using CAFE 3. Mol. Biol. Evol. 30, 1987–1997 (2013).
Google Scholar
Larkin, M. A. et al. ClustalW and ClustalX version 2. Bioinformatics 23, 2947–2948 (2007).
Google Scholar
Subramanian, B., Gao, S., Lercher, M. J., Hu, S. & Chen, W. H. Evolview v3: a webserver for visualization, annotation, and management of phylogenetic trees. Nucleic Acids Res 47, W270–W275 (2019).
Google Scholar
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).
Google Scholar
Kim, D., Landmead, B. & Salzberg, S. L. HISAT: a fast spliced aligner with low memory requirements. Nat. Methods 12, 357–360 (2015).
Google Scholar
Pertea, M. et al. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat. Biotechnol. 33, 290–295 (2015).
Google Scholar
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).
Google Scholar
Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinform. 12, 323 (2011).
Google Scholar
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