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Immunity in Insects pp 3–34Cite as

Characterization of Insect Immune Systems from Genomic Data

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Abstract

Insects face a multitude of threats from the pathogens and parasites they encounter over their life cycles, and they use robust immune systems to defend themselves. This chapter provides a tutorial for the identification and annotation of genes that comprise the immune system from newly sequenced insect genomes. Insect immune responses are orchestrated by the products of a suite of genes responsible for pathogen recognition, signal transduction, and pathogen killing. Many of the genes and proteins underlying these processes can be identified based on sequence homology with related species that have been immunologically characterized. Additional components of the immune response can be identified by transcriptomic analyses to detect genes whose expression changes in response to infection stimulus. Application of our step-by-step protocols for these complementary approaches enables the characterization of insect immune systems from genomic data.

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References

  1. Ligoxygakis P (2017) Advances in insect physiology. volume 52, insect immunity. Academic Press, San Diego

    Google Scholar 

  2. Buchon N, Silverman N, Cherry S (2014) Immunity in Drosophila melanogaster-from microbial recognition to whole-organism physiology. Nat Rev Immunol 14:796–810. https://doi.org/10.1038/nri3763

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Imler J-L (2014) Overview of Drosophila immunity: a historical perspective. Dev Comp Immunol 42:3–15. https://doi.org/10.1016/j.dci.2013.08.018

    Article  CAS  Google Scholar 

  4. Rolff J, Reynolds SE (2009) Insect infection and immunity: evolution, ecology, and mechanisms. Oxford University Press, New York. https://doi.org/10.1093/acprof:oso/9780199551354.001.0001

    Book  Google Scholar 

  5. Ferrandon D, Imler J-L, Hetru C, Hoffmann JA (2007) The Drosophila systemic immune response: sensing and signalling during bacterial and fungal infections. Nat Rev Immunol 7:862–874. https://doi.org/10.1038/nri2194

    Article  CAS  PubMed  Google Scholar 

  6. Lemaitre B, Hoffmann J (2007) The host defense of Drosophila melanogaster. Annu Rev Immunol 25:697–743. https://doi.org/10.1146/annurev.immunol.25.022106.141615

    Article  CAS  PubMed  Google Scholar 

  7. Sackton TB, Lazzaro BP, Clark AG, Wittkopp P (2017) Rapid expansion of immune-related gene families in the house fly, Musca domestica. Mol Biol Evol 34:857–872. https://doi.org/10.1093/molbev/msw285

  8. Barribeau SM, Sadd BM, du Plessis L et al (2015) A depauperate immune repertoire precedes evolution of sociality in bees. Genome Biol 16:83. https://doi.org/10.1186/s13059-015-0628-y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Bartholomay LC, Waterhouse RM, Mayhew GF et al (2010) Pathogenomics of Culex quinquefasciatus and meta-analysis of infection responses to diverse pathogens. Science 330:88–90. https://doi.org/10.1126/science.1193162

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Gerardo NM, Altincicek B, Anselme C et al (2010) Immunity and other defenses in pea aphids, Acyrthosiphon pisum. Genome Biol 11:R21. https://doi.org/10.1186/gb-2010-11-2-r21

    Article  PubMed  PubMed Central  Google Scholar 

  11. Tanaka H, Ishibashi J, Fujita K et al (2008) A genome-wide analysis of genes and gene families involved in innate immunity of Bombyx mori. Insect Biochem Mol Biol 38:1087–1110. https://doi.org/10.1016/j.ibmb.2008.09.001

    Article  CAS  PubMed  Google Scholar 

  12. Sackton TB, Lazzaro BP, Schlenke TA et al (2007) Dynamic evolution of the innate immune system in Drosophila. Nat Genet 39:1461–1468. https://doi.org/10.1038/ng.2007.60

    Article  CAS  PubMed  Google Scholar 

  13. Waterhouse RM, Kriventseva EV, Meister S et al (2007) Evolutionary dynamics of immune-related genes and pathways in disease-vector mosquitoes. Science 316:1738–1743. https://doi.org/10.1126/science.1139862

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Zou Z, Evans JD, Lu Z et al (2007) Comparative genomic analysis of the Tribolium immune system. Genome Biol 8:R177. https://doi.org/10.1186/gb-2007-8-8-r177

    Article  PubMed  PubMed Central  Google Scholar 

  15. Evans JD, Aronstein K, Chen YP et al (2006) Immune pathways and defence mechanisms in honey bees Apis mellifera. Insect Mol Biol 15:645–656. https://doi.org/10.1111/j.1365-2583.2006.00682.x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Christophides GK, Zdobnov E, Barillas-Mury C et al (2002) Immunity-related genes and gene families in Anopheles gambiae. Science 298:159–165. https://doi.org/10.1126/science.1077136

    Article  CAS  PubMed  Google Scholar 

  17. Waterhouse RM, Wyder S, Zdobnov EM (2008) The Aedes aegypti genome: a comparative perspective. Insect Mol Biol 17:1–8. https://doi.org/10.1111/j.1365-2583.2008.00772.x

    Article  CAS  PubMed  Google Scholar 

  18. Olafson PU, Aksoy S, Attardo GM, et al (2019) Functional genomics of the stable fly, Stomoxys calcitrans, reveals mechanisms underlying reproduction, host interactions, and novel targets for pest control. bioRxiv 623009. https://doi.org/10.1101/623009

  19. Altincicek B, Vilcinskas A (2007) Analysis of the immune-inducible transcriptome from microbial stress resistant, rat-tailed maggots of the drone fly Eristalis tenax. BMC Genomics 8:326. https://doi.org/10.1186/1471-2164-8-326

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Sackton TB, Clark AG (2009) Comparative profiling of the transcriptional response to infection in two species of Drosophila by short-read cDNA sequencing. BMC Genomics 10:259. https://doi.org/10.1186/1471-2164-10-259

    Article  PubMed  PubMed Central  Google Scholar 

  21. Sackton TB (2019) Comparative genomics and transcriptomics of host–pathogen interactions in insects: evolutionary insights and future directions. Curr Opin Insect Sci 31:106–113. https://doi.org/10.1016/J.COIS.2018.12.007

    Article  PubMed  Google Scholar 

  22. Ekengren S, Hultmark D (2001) A family of Turandot-related genes in the humoral stress response of Drosophila. Biochem Biophys Res Commun 284:998–1003. https://doi.org/10.1006/bbrc.2001.5067

    Article  CAS  PubMed  Google Scholar 

  23. Brun S, Vidal S, Spellman P et al (2006) The MAPKKK Mekk1 regulates the expression of Turandot stress genes in response to septic injury in Drosophila. Genes Cells 11:397–407. https://doi.org/10.1111/j.1365-2443.2006.00953.x

    Article  CAS  PubMed  Google Scholar 

  24. De Gregorio E, Spellman PT, Rubin GM, Lemaitre B (2001) Genome-wide analysis of the Drosophila immune response by using oligonucleotide microarrays. Proc Natl Acad Sci 98:12590–12595. https://doi.org/10.1073/pnas.221458698

    Article  CAS  Google Scholar 

  25. Troha K, Im JH, Revah J et al (2018) Comparative transcriptomics reveals CrebA as a novel regulator of infection tolerance in D. melanogaster. PLOS Pathog 14:e1006847. https://doi.org/10.1371/journal.ppat.1006847

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Hillyer JF (2016) Insect immunology and hematopoiesis. Dev Comp Immunol 58:102–118. https://doi.org/10.1016/j.dci.2015.12.006

    Article  CAS  PubMed  Google Scholar 

  27. Bartholomay LC, Michel K (2018) Mosquito immunobiology: the intersection of vector health and vector competence. Annu Rev Entomol 63:145–167. https://doi.org/10.1146/annurev-ento-010715-023530

    Article  CAS  PubMed  Google Scholar 

  28. Legeai F, Shigenobu S, Gauthier JP et al (2010) AphidBase: a centralized bioinformatic resource for annotation of the pea aphid genome. Insect Mol Biol 19(Suppl 2):5–12. https://doi.org/10.1111/j.1365-2583.2009.00930.x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Kersey PJ, Allen JE, Allot A et al (2018) Ensembl genomes 2018: an integrated omics infrastructure for non-vertebrate species. Nucleic Acids Res 46:D802–D808. https://doi.org/10.1093/nar/gkx1011

    Article  CAS  PubMed  Google Scholar 

  30. Thurmond J, Goodman JL, Strelets VB et al (2019) FlyBase 2.0: the next generation. Nucleic Acids Res 47:D759–D765. https://doi.org/10.1093/nar/gky1003

    Article  CAS  PubMed  Google Scholar 

  31. Elsik CG, Tayal A, Unni DR et al (2018) Hymenoptera genome database: using HymenopteraMine to enhance genomic studies of hymenopteran insects. Methods Mol Biol 1757:513–556. https://doi.org/10.1007/978-1-4939-7737-6_17

    Article  CAS  PubMed  Google Scholar 

  32. Poelchau MF, Chen MJM, Lin YY, Childers CP (2018) Navigating the i5k workspace@NAL: a resource for arthropod genomes. Methods Mol Biol 1757:557–577. https://doi.org/10.1007/978-1-4939-7737-6_18

    Article  CAS  PubMed  Google Scholar 

  33. Sayers EW, Agarwala R, Bolton EE et al (2019) Database resources of the National Center for Biotechnology Information. Nucleic Acids Res 47:D23–D28. https://doi.org/10.1093/nar/gky1069

    Article  CAS  PubMed  Google Scholar 

  34. Giraldo-Calderón GI, Emrich SJ, MacCallum RM et al (2015) VectorBase: an updated bioinformatics resource for invertebrate vectors and other organisms related with human diseases. Nucleic Acids Res 43:D707–D713. https://doi.org/10.1093/nar/gku1117

    Article  CAS  PubMed  Google Scholar 

  35. Attardo GM, Abd-Alla AMM, Acosta-Serrano A, et al (2019) Comparative genomic analysis of six Glossina genomes, vectors of African trypanosomes. Genome Biol 20:187. https://doi.org/10.1186/s13059-019-1768-2

  36. Li M, Christen JM, Dittmer NT et al (2018) The Manduca sexta serpinome: analysis of serpin genes and proteins in the tobacco hornworm. Insect Biochem Mol Biol 102:21–30. https://doi.org/10.1016/j.ibmb.2018.09.008

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Adelman ZN, Myles KM (2018) The C-type lectin domain gene family in Aedes aegypti and their role in arbovirus infection. Viruses 10:367. https://doi.org/10.3390/v10070367

    Article  PubMed Central  Google Scholar 

  38. Matetovici I, Van Den Abbeele J (2018) Thioester-containing proteins in the tsetse fly (Glossina) and their response to trypanosome infection. Insect Mol Biol 27:414–428. https://doi.org/10.1111/imb.12382

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Yang L, Mei Y, Fang Q et al (2017) Identification and characterization of serine protease inhibitors in a parasitic wasp, Pteromalus puparum. Sci Rep 7:15755. https://doi.org/10.1038/s41598-017-16000-5

  40. Neafsey DE, Waterhouse RM, Abai MR et al (2015) Highly evolvable malaria vectors: the genomes of 16 Anopheles mosquitoes. Science 347:1258522. https://doi.org/10.1126/science.1258522

    Article  PubMed  PubMed Central  Google Scholar 

  41. Terrapon N, Li C, Robertson HM et al (2014) Molecular traces of alternative social organization in a termite genome. Nat Commun 5:3636. https://doi.org/10.1038/ncomms4636

    Article  CAS  PubMed  Google Scholar 

  42. Brucker RM, Funkhouser LJ, Setia S et al (2012) Insect innate immunity database (IIID): an annotation tool for identifying immune genes in insect genomes. PLoS One 7:e45125. https://doi.org/10.1371/journal.pone.0045125

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Bryant B, Ungerer MC, Liu Q et al (2010) A caspase-like decoy molecule enhances the activity of a paralogous caspase in the yellow fever mosquito, Aedes aegypti. Insect Biochem Mol Biol 40:516–523. https://doi.org/10.1016/j.ibmb.2010.04.011

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Waterhouse RM, Povelones M, Christophides GK (2010) Sequence-structure-function relations of the mosquito leucine-rich repeat immune proteins. BMC Genomics 11:531. https://doi.org/10.1186/1471-2164-11-531

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Zou Z, Picheng Z, Weng H et al (2009) A comparative analysis of serpin genes in the silkworm genome. Genomics 93:367–375. https://doi.org/10.1016/j.ygeno.2008.12.010

    Article  CAS  PubMed  Google Scholar 

  46. Camacho C, Coulouris G, Avagyan V et al (2009) BLAST+: architecture and applications. BMC Bioinformatics 10:421. https://doi.org/10.1186/1471-2105-10-421

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Jones P, Binns D, Chang HY et al (2014) InterProScan 5: genome-scale protein function classification. Bioinformatics 30:1236–1240. https://doi.org/10.1093/bioinformatics/btu031

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Mitchell AL, Attwood TK, Babbitt PC et al (2019) InterPro in 2019: improving coverage, classification and access to protein sequence annotations. Nucleic Acids Res 47:D351–D360. https://doi.org/10.1093/nar/gky1100

    Article  CAS  PubMed  Google Scholar 

  49. Löytynoja A (2014) Phylogeny-aware alignment with PRANK. Methods Mol Biol 1079:155–170. https://doi.org/10.1007/978-1-62703-646-7_10

    Article  PubMed  Google Scholar 

  50. Katoh K, Standley DM (2013) MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol 30:772–780. https://doi.org/10.1093/molbev/mst010

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Eddy SR (2011) Accelerated profile HMM searches. PLoS Comput Biol 7:e1002195. https://doi.org/10.1371/journal.pcbi.1002195

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Waterhouse RM (2015) A maturing understanding of the composition of the insect gene repertoire. Curr Opin Insect Sci 7:15–23. https://doi.org/10.1016/j.cois.2015.01.004

    Article  PubMed  Google Scholar 

  53. Waterhouse RM, Seppey M, Simão FA et al (2018) BUSCO applications from quality assessments to gene prediction and phylogenomics. Mol Biol Evol 35:543–548. https://doi.org/10.1093/molbev/msx319

    Article  CAS  PubMed  Google Scholar 

  54. Waterhouse RM, Seppey M, Simão FA, Zdobnov EM (2019) Using BUSCO to assess insect genomic resources. In: Methods Mol. Biol. Humana Press, New York, NY, pp 59–74

    Google Scholar 

  55. Pennisi E (2000) Ideas fly at gene-finding jamboree. Science 287:2182–2184. https://doi.org/10.1126/science.287.5461.2182

    Article  CAS  PubMed  Google Scholar 

  56. Saha S, Hosmani PS, Villalobos-Ayala K et al (2017) Improved annotation of the insect vector of citrus greening disease: biocuration by a diverse genomics community. Database (Oxford) 2017. https://doi.org/10.1093/database/bax032

  57. Hosmani PS, Shippy T, Miller S et al (2019) A quick guide for student-driven community genome annotation. PLoS Comput Biol 15:e1006682. https://doi.org/10.1371/journal.pcbi.1006682

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Ioannidis P, Simao FA, Waterhouse RM et al (2017) Genomic features of the damselfly Calopteryx splendens representing a sister clade to most insect orders. Genome Biol Evol 9:415–430. https://doi.org/10.1093/gbe/evx006

  59. Buels R, Yao E, Diesh CM et al (2016) JBrowse: a dynamic web platform for genome visualization and analysis. Genome Biol 17:66. https://doi.org/10.1186/s13059-016-0924-1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Dunn NA, Unni DR, Diesh C et al (2019) Apollo: Democratizing genome annotation. PLoS Comput Biol 15:e1006790. https://doi.org/10.1371/journal.pcbi.1006790

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Priyam A, Woodcroft BJ, Rai V, et al (2019) Sequenceserver: a modern graphical user interface for custom BLAST databases. Mol Biol Evol 36:2922–2924. https://doi.org/10.1093/molbev/msz185

    Article  PubMed  PubMed Central  Google Scholar 

  62. Kim D, Langmead B, Salzberg SL (2015) HISAT: a fast spliced aligner with low memory requirements. Nat Methods 12:357–360. https://doi.org/10.1038/nmeth.3317

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Dobin A, Davis CA, Schlesinger F et al (2013) STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29:15–21. https://doi.org/10.1093/bioinformatics/bts635

    Article  CAS  PubMed  Google Scholar 

  64. Ramírez F, Ryan DP, Grüning B et al (2016) deepTools2: a next generation web server for deep-sequencing data analysis. Nucleic Acids Res 44:W160–W165. https://doi.org/10.1093/nar/gkw257

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Altenhoff AM, Dessimoz C (2012) Inferring orthology and paralogy. Methods Mol Biol 855:259–279. https://doi.org/10.1007/978-1-61779-582-4_9

    Article  CAS  PubMed  Google Scholar 

  66. Hall BG (2013) Building phylogenetic trees from molecular data with MEGA. Mol Biol Evol 30(5):1229–1235. https://doi.org/10.1093/molbev/mst012

    Article  CAS  PubMed  Google Scholar 

  67. Conesa A, Madrigal P, Tarazona S et al (2016) A survey of best practices for RNA-seq data analysis. Genome Biol 17:13. https://doi.org/10.1186/s13059-016-0881-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Crawford JE, Guelbeogo WM, Sanou A et al (2010) De novo transcriptome sequencing in Anopheles funestus using Illumina RNA-seq technology. PLoS One 5:e14202. https://doi.org/10.1371/journal.pone.0014202

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Haas BJ, Papanicolaou A, Yassour M et al (2013) De novo transcript sequence reconstruction from RNA-seq using the trinity platform for reference generation and analysis. Nat Protoc 8:1494–1512. https://doi.org/10.1038/nprot.2013.084

    Article  CAS  PubMed  Google Scholar 

  70. Holt C, Yandell M (2011) MAKER2: an annotation pipeline and genome-database management tool for second-generation genome projects. BMC Bioinformatics 12:491. https://doi.org/10.1186/1471-2105-12-491

    Article  PubMed  PubMed Central  Google Scholar 

  71. Ching T, Huang S, Garmire LX (2014) Power analysis and sample size estimation for RNA-Seq differential expression. RNA 20:1684–1696. https://doi.org/10.1261/rna.046011.114

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Schurch NJ, Schofield P, Gierliński M et al (2016) How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use? RNA 22:839–851. https://doi.org/10.1261/rna.053959.115

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Robles JA, Qureshi SE, Stephen SJ et al (2012) Efficient experimental design and analysis strategies for the detection of differential expression using RNA-sequencing. BMC Genomics 13:484. https://doi.org/10.1186/1471-2164-13-484

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Todd EV, Black MA, Gemmell NJ (2016) The power and promise of RNA-seq in ecology and evolution. Mol Ecol 25:1224–1241. https://doi.org/10.1111/mec.13526

    Article  CAS  PubMed  Google Scholar 

  75. Khalil S, Jacobson E, Chambers MC, Lazzaro BP (2015) Systemic bacterial infection and immune defense phenotypes in Drosophila melanogaster. J Vis Exp 99:e52613. https://doi.org/10.3791/52613

  76. Taylor K, Kimbrell DA (2007) Host immune response and differential survival of the sexes in Drosophila. Fly (Austin) 1:197–204. https://doi.org/10.4161/fly.5082

    Article  PubMed  Google Scholar 

  77. Schlüns H, Sadd BM, Schmid-Hempel P, Crozier RH (2010) Infection with the trypanosome Crithidia bombi and expression of immune-related genes in the bumblebee Bombus terrestris. Dev Comp Immunol 34:705–709. https://doi.org/10.1016/j.dci.2010.02.002

    Article  Google Scholar 

  78. Bray NL, Pimentel H, Melsted P, Pachter L (2016) Near-optimal probabilistic RNA-seq quantification. Nat Biotechnol 34:525–527. https://doi.org/10.1038/nbt.3519

    Article  CAS  PubMed  Google Scholar 

  79. Pimentel H, Bray NL, Puente S et al (2017) Differential analysis of RNA-seq incorporating quantification uncertainty. Nat Methods 14:687–690. https://doi.org/10.1038/nmeth.4324

    Article  CAS  PubMed  Google Scholar 

  80. Mussabekova A, Daeffler L, Imler J-L (2017) Innate and intrinsic antiviral immunity in Drosophila. Cell Mol Life Sci 74:2039–2054. https://doi.org/10.1007/s00018-017-2453-9

    Article  CAS  PubMed  Google Scholar 

  81. Myllymäki H, Valanne S, Rämet M (2014) The Drosophila imd signaling pathway. J Immunol 192:3455–3462. https://doi.org/10.4049/jimmunol.1303309

    Article  PubMed  Google Scholar 

  82. Myllymäki H, Rämet M (2014) JAK/STAT pathway in Drosophila immunity. Scand J Immunol 79:377–385. https://doi.org/10.1111/sji.12170

    Article  PubMed  Google Scholar 

  83. Valanne S, Wang J-H, Rämet M (2011) The Drosophila toll signaling pathway. J Immunol 186:649–656. https://doi.org/10.4049/jimmunol.1002302

    Article  CAS  PubMed  Google Scholar 

  84. Bosch PS, Makhijani K, Herboso L, et al (2019) Blood cells of adult Drosophila do not expand, but control survival after bacterial infection by induction of Drosocin around their reservoir at the respiratory epithelia. bioRxiv 578864. https://doi.org/10.1101/578864

  85. Buchon N, Poidevin M, Kwon H-M et al (2009) A single modular serine protease integrates signals from pattern-recognition receptors upstream of the Drosophila toll pathway. Proc Natl Acad Sci 106:12442–12447. https://doi.org/10.1073/pnas.0901924106

    Article  CAS  Google Scholar 

  86. Apidianakis Y, Mindrinos MN, Xiao W et al (2005) Profiling early infection responses: Pseudomonas aeruginosa eludes host defenses by suppressing antimicrobial peptide gene expression. Proc Natl Acad Sci 102:2573–2578. https://doi.org/10.1073/pnas.0409588102

    Article  CAS  Google Scholar 

  87. Sackton TB, Werren JH, Clark AG (2013) Characterizing the infection-induced transcriptome of Nasonia vitripennis reveals a preponderance of taxonomically-restricted immune genes. PLoS One 8:e83984. https://doi.org/10.1371/journal.pone.0083984

    Article  PubMed  PubMed Central  Google Scholar 

  88. Gupta SK, Kupper M, Ratzka C et al (2015) Scrutinizing the immune defence inventory of Camponotus floridanus applying total transcriptome sequencing. BMC Genomics 16:540. https://doi.org/10.1186/s12864-015-1748-1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Doublet V, Poeschl Y, Gogol-Döring A et al (2017) Unity in defence: honeybee workers exhibit conserved molecular responses to diverse pathogens. BMC Genomics 18:207. https://doi.org/10.1186/s12864-017-3597-6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Baruzzo G, Hayer KE, Kim EJ et al (2017) Simulation-based comprehensive benchmarking of RNA-seq aligners. Nat Methods 14:135–139. https://doi.org/10.1038/nmeth.4106

    Article  CAS  PubMed  Google Scholar 

  91. Yi L, Pimentel H, Bray NL, Pachter L (2018) Gene-level differential analysis at transcript-level resolution. Genome Biol 19:53. https://doi.org/10.1186/s13059-018-1419-z

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Soneson C, Love MI, Robinson MD (2016) Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Res 4:1521. https://doi.org/10.12688/f1000research.7563.2

    Article  PubMed Central  Google Scholar 

  93. Patro R, Duggal G, Love MI et al (2017) Salmon provides fast and bias-aware quantification of transcript expression. Nat Methods 14:417–419. https://doi.org/10.1038/nmeth.4197

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Li B, Dewey CN (2011) RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12:323. https://doi.org/10.1186/1471-2105-12-323

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Bolger AM, Lohse M, Usadel B (2014) Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30:2114–2120. https://doi.org/10.1093/bioinformatics/btu170

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Gaspar JM (2018) NGmerge: merging paired-end reads via novel empirically-derived models of sequencing errors. BMC Bioinformatics 19:536. https://doi.org/10.1186/s12859-018-2579-2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15:550. https://doi.org/10.1186/s13059-014-0550-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Law CW, Chen Y, Shi W, Smyth GK (2014) Voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol 15:R29. https://doi.org/10.1186/gb-2014-15-2-r29

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Robinson MD, McCarthy DJ, Smyth GK (2010) edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26:139–140. https://doi.org/10.1093/bioinformatics/btp616

    Article  CAS  PubMed  Google Scholar 

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Acknowledgments

RMW acknowledges the Swiss National Science Foundation (grants PP00P3_170664 and CRSII5_186397); the Department of Ecology and Evolution, University of Lausanne; and Swiss Institute of Bioinformatics, Switzerland. BPL acknowledges the Unites States National Institutes of Health (grant R01 AI141385); and the Cornell Institute of Host-Microbe Interactions and Disease, Department of Entomology, Cornell University, Ithaca, New York, USA. TBS acknowledges the Informatics Group, Faculty of Arts and Sciences, Harvard University, Cambridge, Massachusetts, USA.

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Authors and Affiliations

  1. Department of Ecology and Evolution and Swiss Institute of Bioinformatics, University of Lausanne, Lausanne, Switzerland

    Robert M. Waterhouse

  2. Cornell Institute of Host-Microbe Interactions and Disease, Departments of Entomology and Ecology & Evolutionary Biology, Cornell University, Ithaca, NY, USA

    Brian P. Lazzaro

  3. Faculty of Arts and Sciences, Harvard University, Cambridge, MA, USA

    Timothy B. Sackton

Authors
  1. Robert M. Waterhouse
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  2. Brian P. Lazzaro
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  3. Timothy B. Sackton
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Correspondence to Robert M. Waterhouse .

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Editors and Affiliations

  1. Department of Biology, University of Padova, Padova, Italy

    Federica Sandrelli

  2. Department of Biotechnology and Life Sciences, University of Insubria, Varese, Italy

    Gianluca Tettamanti

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Waterhouse, R.M., Lazzaro, B.P., Sackton, T.B. (2020). Characterization of Insect Immune Systems from Genomic Data. In: Sandrelli, F., Tettamanti, G. (eds) Immunity in Insects. Springer Protocols Handbooks. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-0259-1_1

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  • DOI: https://doi.org/10.1007/978-1-0716-0259-1_1

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-0258-4

  • Online ISBN: 978-1-0716-0259-1

  • eBook Packages: Springer Protocols

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