Chromosome-level genome assembly of Bactrocera dorsalis reveals its adaptation and invasion mechanisms
1.Qin, Y.-j. et al. Population structure of a global agricultural invasive pest, Bactrocera dorsalis (Diptera: Tephritidae). Evol. Appl. 11, 1990–2003 (2018).PubMed
PubMed Central
Google Scholar
2.Christenson, L. D. & Foote, R. H. Biology of fruit flies. Annu. Rev. Entomol. 5, 171–192 (1960).
Google Scholar
3.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).CAS
PubMed
Google Scholar
4.Culliney, T. W. The aliens have landed: invasive species threaten Hawaii agriculture. Agric. Hawaii 3, 6–9 (2002).
Google Scholar
5.Cantrell, B., Chadwick, B. & Cahill, A. Fruit Fly Fighters: Eradication of the Papaya Fruit Fly (CSIRO, Collingwood, 2002).6.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).CAS
PubMed
Google Scholar
7.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).
Google Scholar
8.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).PubMed
Google Scholar
9.Li, F. et al. Insect genomes: progress and challenges. Insect Mol. Biol. 28, 739–758 (2019).CAS
PubMed
Google Scholar
10.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).
Google Scholar
11.Dudchenko, O. et al. De novo assembly of the Aedes aegypti genome using Hi-C yields chromosome-length scaffolds. Science 356, 92–95 (2017).CAS
PubMed
PubMed Central
Google Scholar
12.Wu, N. et al. Fall webworm genomes yield insights into rapid adaptation of invasive species. Nat. Ecol. Evol. 3, 105–115 (2019).PubMed
Google Scholar
13.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).PubMed
PubMed Central
Google Scholar
14.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).CAS
Google Scholar
15.Parra, G., Bradnam, K. & Korf, I. CEGMA: a pipeline to accurately annotate core genes in eukaryotic genomes. Bioinformatics 23, 1061–1067 (2007).CAS
PubMed
Google Scholar
16.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).
Google Scholar
17.Ashburner, M. et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25, 25–29 (2000).CAS
PubMed
PubMed Central
Google Scholar
18.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).
Google Scholar
19.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).CAS
PubMed
PubMed Central
Google Scholar
20.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).CAS
PubMed
PubMed Central
Google Scholar
21.Rappoport, N. & Linial, M. Trends in genome dynamics among major orders of insects revealed through variations in protein families. BMC Genomics 16, 583 (2015).PubMed
PubMed Central
Google Scholar
22.Sackton, T. B. et al. Dynamic evolution of the innate immune system in Drosophila. Nat. Genet. 39, 1461–1468 (2007).CAS
PubMed
Google Scholar
23.Stephen, B. H. & David, L. H. Key evolutionary innovations and their ecological mechanisms. Hist. Biol. 10, 151–173 (1995).
Google Scholar
24.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).CAS
PubMed
PubMed Central
Google Scholar
25.Lindquist, S. The heat-shock response. Annu. Rev. Biochem. 55, 1151–1191 (1986).CAS
PubMed
Google Scholar
26.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).CAS
PubMed
Google Scholar
27.Feder, M. E. & Hofmann, G. E. Heat-shock proteins, molecular chaperones, and the stress response. Annu. Rev. Physiol. 61, 243–282 (1999).CAS
PubMed
Google Scholar
28.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).
Google Scholar
29.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).CAS
PubMed
PubMed Central
Google Scholar
30.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).PubMed
PubMed Central
Google Scholar
31.Weinstein, D. J. et al. The genome of a subterrestrial nematode reveals adaptations to heat. Nat. Commun. 10, 5268 (2019).PubMed
PubMed Central
Google Scholar
32.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).CAS
PubMed
PubMed Central
Google Scholar
33.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).
Google Scholar
34.Terblanche, J. S. et al. Ecologically relevant measures of tolerance to potentially lethal temperatures. J. Exp. Biol. 214, 3713–3725 (2011).PubMed
Google Scholar
35.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).PubMed
PubMed Central
Google Scholar
36.Trempolec, N., Dave-Coll, N. & Nebreda, A. R. Snapshot: p38 MAPK signaling. Cell 152, 656–656.e1 (2013).CAS
PubMed
Google Scholar
37.Tatar, M. et al. A mutant Drosophila insulin receptor homolog that extends life-span and impairs neuroendocrine function. Science 292, 107–110 (2001).CAS
PubMed
Google Scholar
38.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).CAS
PubMed
PubMed Central
Google Scholar
39.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).CAS
Google Scholar
40.Xiao, X. P. et al. A Mesh-Duox pathway regulates homeostasis in the insect gut. Nat. Microbiol. 2, 17020 (2017).PubMed
PubMed Central
Google Scholar
41.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).PubMed
PubMed Central
Google Scholar
42.Drew, R. & Yuval, B. Fruit Flies (Tephritidae): Phylogeny and Evolution of Behavior (eds Aluja, M. & Norrbom, A.) 731−749 (CRC Press, 2000).43.Dahanukar, A., Hallem, E. A. & Carlson, J. R. Insect chemoreception. Curr. Opin. Neurobiol. 15, 423–430 (2005).CAS
PubMed
Google Scholar
44.Bargmann, C. I. Comparative chemosensation from receptors to ecology. Nature 444, 295 (2006).CAS
PubMed
Google Scholar
45.Benton, R. Multigene family evolution: perspectives from insect chemoreceptors. Trends Ecol. Evol. 30, 590–600 (2015).PubMed
Google Scholar
46.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).CAS
PubMed
Google Scholar
47.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).
Google Scholar
48.Harris, R. M. & Hofmann, H. A. Seeing is believing: dynamic evolution of gene families. Proc. Natl Acad. Sci. USA 112, 1252–1253 (2015).CAS
PubMed
PubMed Central
Google Scholar
49.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).50.Arguello, J. R. et al. Extensive local adaptation within the chemosensory system following Drosophila melanogaster’s global expansion. Nat. Commun. 7, 11855 (2016).
Google Scholar
51.Li, S. et al. The genomic and functional landscapes of developmental plasticity in the American cockroach. Nat. Commun. 9, 1008 (2018).PubMed
PubMed Central
Google Scholar
52.Vontas, J. et al. Insecticide resistance in Tephritid flies. Pestic. Biochem. Physiol. 100, 199–205 (2011).CAS
Google Scholar
53.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).
Google Scholar
54.Scott, J. G. Cytochromes P450 and insecticide resistance. Insect Biochem. Mol. Biol. 29, 757–777 (1999).CAS
PubMed
Google Scholar
55.Rane, R. V. et al. Detoxifying enzyme complements and host use phenotypes in 160 insect species. Curr. Opin. Insect Sci. 31, 131–138 (2019).PubMed
Google Scholar
56.Pendleton, M. et al. Assembly and diploid architecture of an individual human genome via single-molecule technologies. Nat. Methods 12, 780–786 (2015).CAS
PubMed
PubMed Central
Google Scholar
57.Belton, J. M. et al. Hi-C: a comprehensive technique to capture the conformation of genomes. Methods 58, 268–276 (2012).CAS
PubMed
Google Scholar
58.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).
Google Scholar
59.Burton, J. N. et al. Chromosome-scale scaffolding of de novo genome assemblies based on chromatin interactions. Nat. Biotechnol. 31, 1119–1125 (2013).CAS
PubMed
PubMed Central
Google Scholar
60.Servant, N. et al. HiC-Pro: an optimized and flexible pipeline for Hi-C data processing. Genome Biol. 16, 1–11 (2015).
Google Scholar
61.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).CAS
PubMed
PubMed Central
Google Scholar
62.Walker, B. J. et al. Pilon: an integrated tool for comprehensive microbial variant detection and genome assembly improvement. PLoS One 9, e112963 (2014).PubMed
PubMed Central
Google Scholar
63.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).PubMed
PubMed Central
Google Scholar
64.Chin, C. S. et al. Phased diploid genome assembly with single-molecule real-time sequencing. Nat. Methods 13, 1050–1054 (2016).CAS
PubMed
PubMed Central
Google Scholar
65.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).CAS
PubMed
Google Scholar
66.Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows−Wheeler transform. Bioinformatics 25, 1754–1760 (2009).CAS
PubMed
PubMed Central
Google Scholar
67.Worley, K. C. et al. Improving genomes using long reads and PBJelly 2. In International Plant & Animal Genome Conference XXI (2014).68.Krzywinski, M. et al. Circos: An information aesthetic for comparative genomics. Genome Res. 19, 1639–1645 (2009).CAS
PubMed
PubMed Central
Google Scholar
69.Burge, C. & Karlin, S. Prediction of complete gene structures in human genomic DNA. J. Mol. Biol. 268, 78–94 (1997).CAS
PubMed
Google Scholar
70.Stanke, M. & Waack, S. Gene prediction with a hidden Markov model and a new intron submodel. Bioinformatics 19, ii215–ii225 (2003).PubMed
Google Scholar
71.Korf, I. Gene finding in novel genomes. BMC Bioinform. 5, 59 (2004).
Google Scholar
72.Majoros, W. H., Pertea, M. & Salzberg, S. L. TigrScan and GlimmerHMM: two open source ab initio eukaryotic gene-finders. Bioinformatics 20, 2878–2879 (2004).CAS
PubMed
Google Scholar
73.Blanco, E., Parra, G. & Guigó, R. Using geneid to identify genes. Curr. Protoc. Bioinform. 18, 4.3.1–4.3.28 (2007).
Google Scholar
74.Keilwagen, J. et al. Using intron position conservation for homology-based gene prediction. Nucleic Acids Res. 44, e89–e89 (2016).PubMed
PubMed Central
Google Scholar
75.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).PubMed
PubMed Central
Google Scholar
76.Haas, B. J. et al. Automated eukaryotic gene structure annotation using EVidenceModeler and the Program to Assemble Spliced Alignments. Genome Biol. 9, R7 (2008).PubMed
PubMed Central
Google Scholar
77.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).CAS
PubMed
PubMed Central
Google Scholar
78.Griffiths-Jones, S. et al. Rfam: annotating non-coding RNAs in complete genomes. Nucleic Acids Res. 33, D121–D124 (2005).CAS
PubMed
Google Scholar
79.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).CAS
PubMed
Google Scholar
80.Nawrocki, E. P. & Eddy, S. R. Infernal 1.1: 100-fold faster RNA homology searches. Bioinformatics 29, 2933–2935 (2013).CAS
PubMed
PubMed Central
Google Scholar
81.Edgar, R. C. & Myers, E. W. PILER: Identification and classification of genomic repeats. Bioinformatics 21, i152–i158 (2005).CAS
PubMed
Google Scholar
82.Price, A. L., Jones, N. C. & Pevzner, P. A. De novo identification of repeat families in large genomes. Bioinformatics 21, i351–i358 (2005).CAS
PubMed
Google Scholar
83.Xu, Z. & Wang, H. LTR_FINDER: an efficient tool for the prediction of full-length LTR retrotransposons. Nucleic Acids Res. 35, W265–W268 (2007).PubMed
PubMed Central
Google Scholar
84.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).PubMed
PubMed Central
Google Scholar
85.Jurka, J. et al. Repbase Update, a database of eukaryotic repetitive elements. Cytogenet Genome Res. 110, 462–467 (2005).CAS
PubMed
Google Scholar
86.Wicker, T. et al. A unified classification system for eukaryotic transposable elements. Nat. Rev. Genet. 8, 973–982 (2007).CAS
PubMed
Google Scholar
87.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).
Google Scholar
88.Wang, Y. et al. MCScanX: a toolkit for detection and evolutionary analysis of gene synteny and collinearity. Nucleic Acids Res. 40, e49 (2012).CAS
PubMed
PubMed Central
Google Scholar
89.Birney, E., Clamp, M. & Durbin, R. GeneWise and Genomewise. Genome Res. 14, 988–995 (2004).CAS
PubMed
PubMed Central
Google Scholar
90.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).CAS
PubMed
PubMed Central
Google Scholar
91.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).CAS
PubMed
Google Scholar
92.Kanehisa, M. & Goto, S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27–30 (2000).CAS
PubMed
PubMed Central
Google Scholar
93.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).CAS
PubMed
PubMed Central
Google Scholar
94.Boeckmann, B. et al. The SWISS-PROT protein KnowledgeBase and its supplement TrEMBL in 2003. Nucleic Acids Res. 31, 365–370 (2003).CAS
PubMed
PubMed Central
Google Scholar
95.Marchler-Bauer, A. et al. CDD: a Conserved Domain Database for the functional annotation of proteins. Nucleic Acids Res. 39, D225–D229 (2011).CAS
PubMed
Google Scholar
96.Conesa, A. et al. Blast2GO: a universal tool for annotation, visualization, and analysis in functional genomics research. Bioinformatics 21, 3674–3676 (2005).CAS
Google Scholar
97.Dimmer, E. C. et al. The UniProt-GO annotation database in 2011. Nucleic Acids Res. 40, D565–D570 (2012).CAS
PubMed
Google Scholar
98.Bairoch, A. PROSITE-a dictionary of sites and patterns in proteins. Nucleic Acids Res. 19, 2241–2245 (1991).CAS
PubMed
PubMed Central
Google Scholar
99.Attwood, T. K. & Beck, M. E. Prints-a protein motif fingerprint database. Protein Eng. Des. Sel. 7, 841–848 (1994).CAS
Google Scholar
100.Zdobnov, E. M. & Apweiler, R. InterProScan-an integration platform for the signature-recognition methods in InterPro. Bioinformatics 17, 847–848 (2001).CAS
PubMed
Google Scholar
101.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).CAS
PubMed
PubMed Central
Google Scholar
102.Haft, D. H., Selengut, J. D. & White, O. The TIGRFAMs database of protein families. Nucleic Acids Res. 31, 371–373 (2003).CAS
PubMed
PubMed Central
Google Scholar
103.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).CAS
PubMed
PubMed Central
Google Scholar
104.Letunic, I. et al. SMART 4.0: towards genomic data integration. Nucleic Acids Res. 32, D142–D144 (2004).CAS
PubMed
PubMed Central
Google Scholar
105.Wu, C. H. et al. PIRSF: family classification system at the Protein Information Resource. Nucleic Acids Res. 32, D112–D114 (2004).CAS
PubMed
PubMed Central
Google Scholar
106.Bru, C. et al. The ProDom database of protein domain families: more emphasis on 3D. Nucleic Acids Res. 33, D212–D215 (2005).CAS
PubMed
Google Scholar
107.Finn, R. D. et al. Pfam: clans, web tools, and services. Nucleic Acids Res. 34, D247–D251 (2006).CAS
PubMed
Google Scholar
108.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).CAS
PubMed
Google Scholar
109.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).CAS
PubMed
Google Scholar
110.Emms, D. M. & Kelly, S. OrthoFinder: phylogenetic orthology inference for comparative genomics. Genome Biol. 20, 238 (2019).PubMed
PubMed Central
Google Scholar
111.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).CAS
Google Scholar
112.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).CAS
PubMed
PubMed Central
Google Scholar
113.Puttick, M. N. MCMCtreeR: functions to prepare MCMCtree analyses and visualize posterior ages on trees. Bioinformatics 35, 5321–5322 (2019).CAS
PubMed
Google Scholar
114.Yang, Z. H. PAML: a program package for phylogenetic analysis by maximum likelihood. Bioinformatics 13, 555–556 (1997).CAS
Google Scholar
115.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).CAS
PubMed
Google Scholar
116.Larkin, M. A. et al. ClustalW and ClustalX version 2. Bioinformatics 23, 2947–2948 (2007).CAS
PubMed
Google Scholar
117.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).CAS
PubMed
PubMed Central
Google Scholar
118.Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).CAS
PubMed
PubMed Central
Google Scholar
119.Kim, D., Landmead, B. & Salzberg, S. L. HISAT: a fast spliced aligner with low memory requirements. Nat. Methods 12, 357–360 (2015).CAS
PubMed
PubMed Central
Google Scholar
120.Pertea, M. et al. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat. Biotechnol. 33, 290–295 (2015).CAS
PubMed
PubMed Central
Google Scholar
121.Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).CAS
PubMed
PubMed Central
Google Scholar
122.Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinform. 12, 323 (2011).CAS
Google Scholar More
