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Computational Analysis of HTS Data and Its Application in Plant Pathology

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Plant Pathology

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2536))

Abstract

High-throughput sequencing is a basic tool of biological research, and it is extensively used in plant pathology projects. Here, we describe how to handle data coming from a variety of sequencing experiments, focusing on the analysis of Illumina reads. We describe how to perform genome assembly and annotation with DNA reads, correctly analyze RNA-seq data to discover differentially expressed genes, handle amplicon sequencing data from microbial communities, and utilize small RNA sequencing data to predict miRNA sequences and their putative targets.

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References

  1. Tan K, Ipcho SVS, Trengove RD et al (2009) Assessing the impact of transcriptomics, proteomics and metabolomics on fungal phytopathology. Mol Plant Pathol 10:703–715

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Adams IP, Fox A, Boonham N et al (2018) The impact of high throughput sequencing on plant health diagnostics. Eur J Plant Pathol 152(4):909–919. https://doi.org/10.1007/s10658-018-1570-0

    Article  CAS  Google Scholar 

  3. Sarethy IP, Saharan A (2021) Genomics, proteomics and transcriptomics in the biological control of plant pathogens: a review. Indian Phytopathol 74(1):3–12

    Article  Google Scholar 

  4. Villamor DE V, Ho T, Al Rwahnih M et al (2019) High throughput sequencing for plant virus detection and discovery. Phytopathology 109:716–725

    Article  PubMed  Google Scholar 

  5. Rhoads A, Au KF (2015) PacBio sequencing and its applications. Genomics Proteomics Bioinformatics 13:278–289

    Article  PubMed  PubMed Central  Google Scholar 

  6. Lu H, Giordano F, Ning Z (2016) Oxford Nanopore MinION sequencing and genome assembly. Genomics Proteomics Bioinformatics 14:265–279

    Article  PubMed  PubMed Central  Google Scholar 

  7. Menegon M, Cantaloni C, Rodriguez-Prieto A et al (2017) On site DNA barcoding by nanopore sequencing. PLoS One. https://doi.org/10.1371/journal.pone.0184741

  8. Wiemann P, Sieber CMK, Von Bargen KW et al (2013) Deciphering the cryptic genome: genome-wide analyses of the rice pathogen Fusarium fujikuroi reveal complex regulation of secondary metabolism and novel metabolites. PLoS Pathog 9:e1003475

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Shimizu KK, Copetti D, Okada M et al (2021) De novo genome assembly of the Japanese wheat cultivar Norin 61 highlights functional variation in flowering time and fusarium-resistant genes in east Asian genotypes. Plant Cell Physiol 62:8–27

    Article  CAS  PubMed  Google Scholar 

  10. Karlsson M, Durling MB, Choi J et al (2015) Insights on the evolution of mycoparasitism from the genome of clonostachys rosea. Genome Biol Evol 7(2):465–480. https://doi.org/10.1093/gbe/evu292

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Matić S, Bagnaresi P, Biselli C et al (2016) Comparative transcriptome profiling of resistant and susceptible rice genotypes in response to the seedborne pathogen Fusarium fujikuroi. BMC Genomics 17:608. https://doi.org/10.1186/s12864-016-2925-6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Zhang ET, Zhang H, Tang W (2021) Transcriptomic analysis of wheat seedling responses to the systemic acquired resistance inducer n-hydroxypipecolic acid. Front Microbiol 12:94

    CAS  Google Scholar 

  13. Zhang W, Gui Y, Short DPG et al (2018) Verticillium dahliae transcription factor VdFTF1 regulates the expression of multiple secreted virulence factors and is required for full virulence in cotton. Mol Plant Pathol 19:841–857

    Article  CAS  PubMed  Google Scholar 

  14. Nygren K, Dubey M, Zapparata A et al (2018) The mycoparasitic fungus Clonostachys rosea responds with both common and specific gene expression during interspecific interactions with fungal prey. Evol Appl 11:931–949. https://doi.org/10.1111/eva.12609

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Tedersoo L, Drenkhan R, Anslan S et al (2019) High-throughput identification and diagnostics of pathogens and pests: overview and practical recommendations. Mol Ecol Resour 19(1):47–76

    Article  PubMed  Google Scholar 

  16. Piombo E, Abdelfattah A, Droby S et al (2021) Metagenomics approaches for the detection and surveillance of emerging and recurrent plant pathogens. Microorganisms 9:188

    Article  PubMed  PubMed Central  Google Scholar 

  17. Siegel-Hertz K, Edel-Hermann V, Chapelle E et al (2018) Comparative microbiome analysis of a Fusarium wilt suppressive soil and a Fusarium wilt conducive soil from the Châteaurenard region. Front Microbiol 9:568

    Article  PubMed  PubMed Central  Google Scholar 

  18. Vergine M, Meyer JB, Cardinale M et al (2020) The Xylella fastidiosa-resistant olive cultivar “Leccino” has stable endophytic microbiota during the olive quick decline syndrome (OQDS). Pathogens 9:35

    Article  CAS  Google Scholar 

  19. Song L, Fang Y, Chen L et al (2021) Role of non-coding RNAs in plant immunity. Plant Commun 2(3):100180

    Article  PubMed  PubMed Central  Google Scholar 

  20. Yang X, Zhang L, Yang Y et al (2021) miRNA mediated regulation and interaction between plants and pathogens. Int J Mol Sci 22:2913

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Chen R, Jiang N, Jiang Q et al (2014) Exploring microRNA-like small RNAs in the filamentous fungus Fusarium oxysporum. PLoS One 9:e104956. https://doi.org/10.1371/journal.pone.0104956

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Son H, Park AR, Lim JY et al (2017) Genome-wide exonic small interference RNA-mediated gene silencing regulates sexual reproduction in the homothallic fungus Fusarium graminearum. PLoS Genet 13:e1006595. https://doi.org/10.1371/journal.pgen.1006595

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. **a Z, Wang Z, Kav NNV et al (2020) Characterization of microRNA-like RNAs associated with sclerotial development in Sclerotinia sclerotiorum. Fungal Genet Biol 144:103471. https://doi.org/10.1016/j.fgb.2020.103471

    Article  CAS  PubMed  Google Scholar 

  24. Blankenberg D, Gordon A, Von Kuster G et al (2010) Manipulation of FASTQ data with galaxy. Bioinformatics 26(14):1783–1785. https://doi.org/10.1093/bioinformatics/btq281

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Bushnell B (2019) BBTools: a suite of fast, multithreaded bioinformatics tools designed for analysis of DNA and RNA sequence data. Joint Genome Institute, Berkeley

    Google Scholar 

  26. 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 

  27. Krueger F, Ewels P, Schuster-Boeckler B Taking appropriate QC measures for RRBS-type or other -Seq applications with Trim Galore! https://github.com/FelixKrueger/TrimGalore/blob/master/Docs/Trim_Galore_User_Guide.md

  28. Andrews S FastQC: a quality control tool for high throughput sequence data. https://www.bioinformatics.babraham.ac.uk/projects/fastqc/

  29. Compeau PEC, Pevzner PA, Tesler G (2011) How to apply de Bruijn graphs to genome assembly. Nat Biotechnol 29:987–991

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Bankevich A, Nurk S, Antipov D et al (2012) SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol 19:455–477

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Zerbino DR, Birney E (2008) Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Genome Res 18:821–829

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Simpson JT, Wong K, Jackman SD et al (2009) ABySS: a parallel assembler for short read sequence data. Genome Res 19:1117–1123

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Gurevich A, Saveliev V, Vyahhi N, Tesler G (2013) QUAST: quality assessment tool for genome assemblies. Bioinformatics 29:1072–1075

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Paulino D, Warren RL, Vandervalk BP et al (2015) Sealer: a scalable gap-closing application for finishing draft genomes. BMC Bioinformatics 16:1–8

    Article  Google Scholar 

  35. Simão FA, Waterhouse RM, Ioannidis P et al (2015) BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics 31(19):3210–3212. https://doi.org/10.1093/bioinformatics/btv351

    Article  CAS  PubMed  Google Scholar 

  36. Flynn JM, Hubley R, Goubert C et al (2020) RepeatModeler2 for automated genomic discovery of transposable element families. Proc Natl Acad Sci 117:9451–9457

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Tempel S (2012) Using and understanding RepeatMasker. In: Mobile genetic elements. Springer, pp 29–51

    Chapter  Google Scholar 

  38. Stanke M, Keller O, Gunduz I et al (2006) AUGUSTUS: ab initio prediction of alternative transcripts. Nucleic Acids Res 34:W435–W439

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Stanke M, Morgenstern B (2005) AUGUSTUS: a web server for gene prediction in eukaryotes that allows user-defined constraints. Nucleic Acids Res 33:W465–W467

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Campbell MS, Holt C, Moore B, Yandell M (2014) Genome annotation and curation using MAKER and MAKER-P. Curr Protoc Bioinforma 48:4–11

    Article  Google Scholar 

  41. Hoff KJ, Lomsadze A, Borodovsky M, Stanke M (2019) Whole-genome annotation with BRAKER. In: Gene prediction. Springer, pp 65–95

    Google Scholar 

  42. Borodovsky M, Mills R, Besemer J, Lomsadze A (2003) Prokaryotic gene prediction using GeneMark and GeneMark. hmm. Curr Protoc Bioinforma 1:4–5

    Google Scholar 

  43. Conesa A, Götz S, García-Gómez JM et al (2005) Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics 21:3674–3676. https://doi.org/10.1093/bioinformatics/bti610

    Article  CAS  PubMed  Google Scholar 

  44. 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 

  45. 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 

  46. Wen G (2017) A simple process of RNA-sequence analyses by Hisat2, Htseq and DESeq2. In: Proceedings of the 2017 International Conference on Biomedical Engineering and Bioinformatics, pp 11–15

    Chapter  Google Scholar 

  47. Liao Y, Smyth GK, Shi W (2014) FeatureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30:923–930. https://doi.org/10.1093/bioinformatics/btt656

    Article  CAS  PubMed  Google Scholar 

  48. Anders S, Pyl PT, Huber W (2015) HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31:166–169

    Article  CAS  PubMed  Google Scholar 

  49. Love MI, Anders S, Huber W (2014) Differential analysis of count data – the DESeq2 package. Genome Biol 15:10–1186

    CAS  Google Scholar 

  50. 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 

  51. Allaire J (2012) RStudio: integrated development environment for R. Boston, MA 770:394

    Google Scholar 

  52. Rivers AR, Weber KC, Gardner TG et al (2018) ITSxpress: software to rapidly trim internally transcribed spacer sequences with quality scores for marker gene analysis. F1000Res 7:1418

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Estaki M, Jiang L, Bokulich NA et al (2020) QIIME 2 enables comprehensive end-to-end analysis of diverse microbiome data and comparative studies with publicly available data. Curr Protoc Bioinforma 70:e100

    Article  Google Scholar 

  54. Martin M (2011) Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J 17(1):10–12. https://doi.org/10.14806/ej.17.1.200

    Article  Google Scholar 

  55. Callahan BJ, McMurdie PJ, Rosen MJ et al (2016) DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods 13:581-583. https://doi.org/10.1038/nmeth.3869

  56. Vázquez-Baeza Y, Pirrung M, Gonzalez A, Knight R (2013) EMPeror: a tool for visualizing high-throughput microbial community data. Gigascience 2:16. https://doi.org/10.1186/2047-217X-2-16

    Article  PubMed  PubMed Central  Google Scholar 

  57. Abarenkov K, Nilsson RH, Larsson KH et al (2010) The UNITE database for molecular identification of fungi - recent updates and future perspectives. New Phytol 186:281–285. https://doi.org/10.1111/j.1469-8137.2009.03160.x

    Article  PubMed  Google Scholar 

  58. Quast C, Pruesse E, Yilmaz P et al (2013) The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res 41:590–596. https://doi.org/10.1093/nar/gks1219

    Article  CAS  Google Scholar 

  59. Mandal S, Van Treuren W, White RA et al (2015) Analysis of composition of microbiomes: a novel method for studying microbial composition. Microb Ecol Health Dis 26:27663

    PubMed  Google Scholar 

  60. Fernandes AD, Reid JN, Macklaim JM et al (2014) Unifying the analysis of high-throughput sequencing datasets: characterizing RNA-seq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis. Microbiome 2:1–13

    Article  CAS  Google Scholar 

  61. Nguyen NH, Song Z, Bates ST et al (2016) FUNGuild: an open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecol 20:241–248

    Article  Google Scholar 

  62. Bushnell B, Rood J, Singer E (2017) BBTools software package. PLoS One

    Google Scholar 

  63. Kopylova E, Noé L, Touzet H (2012) SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics 28:3211–3217. https://doi.org/10.1093/bioinformatics/bts611

    Article  CAS  PubMed  Google Scholar 

  64. Paschoal AR, Maracaja-Coutinho V, Setubal JC et al (2012) Non-coding transcription characterization and annotation: a guide and web resource for non-coding RNA databases. RNA Biol 9:274–282. https://doi.org/10.4161/rna.19352

    Article  CAS  PubMed  Google Scholar 

  65. Zhao Y, Kuang Z, Wang Y et al (2021) MicroRNA annotation in plants: current status and challenges. Brief Bioinform

    Google Scholar 

  66. Devers EA, Branscheid A, May P, Kra**ski F (2011) Stars and symbiosis: Microrna- and microrna*-mediated transcript cleavage involved in arbuscular mycorrhizal symbiosis. Plant Physiol 156:1990–2010. https://doi.org/10.1104/pp.111.172627

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Zhou J, Fu Y, **e J et al (2012) Identification of microRNA-like RNAs in a plant pathogenic fungus Sclerotinia sclerotiorum by high-throughput sequencing. Mol Gen Genomics 287:275–282. https://doi.org/10.1007/s00438-012-0678-8

    Article  CAS  Google Scholar 

  68. Carreras-Villaseñor N, Esquivel-Naranjo EU, Villalobos-Escobedo JM et al (2013) The RNAi machinery regulates growth and development in the filamentous fungus Trichoderma atroviride. Mol Microbiol 89(1):96–112. https://doi.org/10.1111/mmi.12261

    Article  CAS  PubMed  Google Scholar 

  69. Wang L, Xu X, Yang J et al (2018) Integrated microRNA and mRNA analysis in the pathogenic filamentous fungus Trichophyton rubrum. BMC Genomics 19:1–14. https://doi.org/10.1186/s12864-018-5316-3

    Article  CAS  Google Scholar 

  70. Kang K, Zhong J, Jiang L et al (2013) Identification of microRNA-Like RNAs in the Filamentous Fungus Trichoderma reesei by Solexa Sequencing. PLoS One 8:e76288. https://doi.org/10.1371/journal.pone.0076288

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Friedländer MR, MacKowiak SD, Li N et al (2012) MiRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades. Nucleic Acids Res 40(1):37–52. https://doi.org/10.1093/nar/gkr688

    Article  CAS  PubMed  Google Scholar 

  72. Rehmsmeier M, Steffen P, Höchsmann M, Giegerich R (2004) Fast and effective prediction of microRNA/target duplexes. RNA 10:1507–1517

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Zuker M (2003) Mfold web server for nucleic acid folding and hybridization prediction. Nucleic Acids Res 31:3406–3415

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Moxon S, Schwach F, Dalmay T et al (2008) A toolkit for analysing large-scale plant small RNA datasets. Bioinformatics 24:2252–2253

    Article  CAS  PubMed  Google Scholar 

  75. Jones-Rhoades MW, Bartel DP (2004) Computational identification of plant microRNAs and their targets, including a stress-induced miRNA. Mol Cell 14:787–799

    Article  CAS  PubMed  Google Scholar 

  76. Kozomara A, Birgaoanu M, Griffiths-Jones S (2019) MiRBase: from microRNA sequences to function. Nucleic Acids Res 47:155–162. https://doi.org/10.1093/nar/gky1141

    Article  CAS  Google Scholar 

  77. Griffiths-Jones S, Bateman A, Marshall M et al (2003) Rfam: an RNA family database. Nucleic Acids Res 31:439–441

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Remita MA, Lord E, Agharbaoui Z et al (2016) A novel comprehensive wheat miRNA database, including related bioinformatics software. Curr Plant Biol 7:31–33

    Article  Google Scholar 

  79. Peterson SM, Thompson JA, Ufkin ML et al (2014) Common features of microRNA target prediction tools. Front Genet 5:23

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  80. Riffo-Campos ÁL, Riquelme I, Brebi-Mieville P (2016) Tools for sequence-based miRNA target prediction: what to choose? Int J Mol Sci 17:1987

    Article  PubMed Central  CAS  Google Scholar 

  81. Mishra AK, Duraisamy GS, Matoušek J (2015) Discovering microRNAs and their targets in plants. CRC Crit Rev Plant Sci 34:553–571

    Article  CAS  Google Scholar 

  82. Mourrain P, Béclin C, Elmayan T et al (2000) Arabidopsis SGS2 and SGS3 genes are required for posttranscriptional gene silencing and natural virus resistance. Cell 101:533–542

    Article  CAS  PubMed  Google Scholar 

  83. Tang G, Reinhart BJ, Bartel DP, Zamore PD (2003) A biochemical framework for RNA silencing in plants. Genes Dev 17:49–63

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Liu S-R, Zhou J-J, Hu C-G et al (2017) MicroRNA-mediated gene silencing in plant defense and viral counter-defense. Front Microbiol 8:1801

    Article  PubMed  PubMed Central  Google Scholar 

  85. Enright A, John B, Gaul U et al (2003) MicroRNA targets in Drosophila. Genome Biol 4:1–27

    Article  Google Scholar 

  86. Kertesz M, Iovino N, Unnerstall U et al (2007) The role of site accessibility in microRNA target recognition. Nat Genet 39:1278–1284

    Article  CAS  PubMed  Google Scholar 

  87. Sturm M, Hackenberg M, Langenberger D, Frishman D (2010) TargetSpy: a supervised machine learning approach for microRNA target prediction. BMC Bioinformatics 11:1–17

    Article  CAS  Google Scholar 

  88. Dai X, Zhuang Z, Zhao PX (2018) psRNATarget: a plant small RNA target analysis server (2017 release). Nucleic Acids Res 46:W49–W54

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Bonnet E, He Y, Billiau K, Van de Peer Y (2010) TAPIR, a web server for the prediction of plant microRNA targets, including target mimics. Bioinformatics 26:1566–1568

    Article  CAS  PubMed  Google Scholar 

  90. Wu H-J, Ma Y-K, Chen T et al (2012) PsRobot: a web-based plant small RNA meta-analysis toolbox. Nucleic Acids Res 40:W22–W28

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Fahlgren N, Howell MD, Kasschau KD et al (2007) High-throughput sequencing of Arabidopsis microRNAs: evidence for frequent birth and death of MIRNA genes. PLoS One 2:e219

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  92. Rueda A, Barturen G, Lebrón R et al (2015) SRNAtoolbox: an integrated collection of small RNA research tools. Nucleic Acids Res 43:473. https://doi.org/10.1093/nar/gkv555

    Article  CAS  Google Scholar 

  93. Farh KK-H, Grimson A, Jan C et al (2005) The widespread impact of mammalian MicroRNAs on mRNA repression and evolution. Science 310:1817–1821

    Article  CAS  PubMed  Google Scholar 

  94. Guo H, Ingolia NT, Weissman JS, Bartel DP (2010) Mammalian microRNAs predominantly act to decrease target mRNA levels. Nature 466:835–840

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Iwakawa H, Tomari Y (2013) Molecular insights into microRNA-mediated translational repression in plants. Mol Cell 52:591–601

    Article  CAS  PubMed  Google Scholar 

  96. Llave C, **e Z, Kasschau KD, Carrington JC (2002) Cleavage of Scarecrow-like mRNA targets directed by a class of Arabidopsis miRNA. Science 297:2053–2056

    Article  CAS  PubMed  Google Scholar 

  97. Addo-Quaye C, Eshoo TW, Bartel DP, Axtell MJ (2008) Endogenous siRNA and miRNA targets identified by sequencing of the Arabidopsis degradome. Curr Biol 18:758–762

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. German MA, Pillay M, Jeong D-H et al (2008) Global identification of microRNA–target RNA pairs by parallel analysis of RNA ends. Nat Biotechnol 26:941–946

    Article  CAS  PubMed  Google Scholar 

  99. Gregory BD, O’Malley RC, Lister R et al (2008) A link between RNA metabolism and silencing affecting Arabidopsis development. Dev Cell 14:854–866

    Article  CAS  PubMed  Google Scholar 

  100. Addo-Quaye C, Miller W, Axtell MJ (2009) CleaveLand: a pipeline for using degradome data to find cleaved small RNA targets. Bioinformatics 25:130–131

    Article  CAS  PubMed  Google Scholar 

  101. Zheng Y, Li Y-F, Sunkar R, Zhang W (2012) SeqTar: an effective method for identifying microRNA guided cleavage sites from degradome of polyadenylated transcripts in plants. Nucleic Acids Res 40:e28–e28

    Article  CAS  PubMed  Google Scholar 

  102. Thody J, Folkes L, Medina-Calzada Z et al (2018) PAREsnip2: a tool for high-throughput prediction of small RNA targets from degradome sequencing data using configurable targeting rules. Nucleic Acids Res 46(17):8730–8739. https://doi.org/10.1093/nar/gky609

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Parks DH, Imelfort M, Skennerton CT et al (2015) CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res 25(7):1043–1055. https://doi.org/10.1101/gr.186072.114

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Bateman A, Martin MJ, O’Donovan C et al (2015) UniProt: a hub for protein information. Nucleic Acids Res 43(D1):D204–D212. https://doi.org/10.1093/nar/gku989

    Article  CAS  Google Scholar 

  105. Lorrai R, Ferrari S (2021) Host cell wall damage during pathogen infection: mechanisms of perception and role in plant-pathogen interactions. Plan Theory 10:399

    CAS  Google Scholar 

  106. Bellincampi D, Cervone F, Lionetti V (2014) Plant cell wall dynamics and wall-related susceptibility in plant–pathogen interactions. Front Plant Sci 5:228

    Article  PubMed  PubMed Central  Google Scholar 

  107. Piombo E, Sela N, Wisniewski M et al (2018) Genome sequence, assembly and characterization of two Metschnikowia fructicola strains used as biocontrol agents of postharvest diseases. Front Microbiol 9:593. https://doi.org/10.3389/fmicb.2018.00593

    Article  PubMed  PubMed Central  Google Scholar 

  108. Mukhopadhyay R, Kumar D (2020) Trichoderma: a beneficial antifungal agent and insights into its mechanism of biocontrol potential. Egypt J Biol Pest Control 30(1):1–8

    Article  Google Scholar 

  109. Tzelepis G, Dubey M, Jensen DF, Karlsson M (2015) Identifying glycoside hydrolase family 18 genes in the mycoparasitic fungal species clonostachys rosea. Microbiology 161(7):1407–1419. https://doi.org/10.1099/mic.0.000096

    Article  CAS  PubMed  Google Scholar 

  110. Datta SK, Muthukrishnan S (1999) Pathogenesis-related proteins in plants. CRC Press

    Book  Google Scholar 

  111. Sharma N, Sharma KP, Gaur RK, Gupta VK (2011) Role of chitinase in plant defense. Asian J Biochem 6:29–37

    Article  CAS  Google Scholar 

  112. Zhang H, Yohe T, Huang L et al (2018) DbCAN2: a meta server for automated carbohydrate-active enzyme annotation. Nucleic Acids Res 46:95–101. https://doi.org/10.1093/nar/gky418

    Article  CAS  Google Scholar 

  113. Blin K, Shaw S, Steinke K et al (2019) AntiSMASH 5.0: updates to the secondary metabolite genome mining pipeline. Nucleic Acids Res 47:81–87. https://doi.org/10.1093/nar/gkz310

    Article  CAS  Google Scholar 

  114. Suga H, Arai M, Fukasawa E et al (2019) Genetic differentiation associated with fumonisin and gibberellin production in Japanese Fusarium fujikuroi. Appl Environ Microbiol 85(1):e02414–e02418

    Article  CAS  PubMed  Google Scholar 

  115. Fatema U, Broberg A, Jensen DF et al (2018) Functional analysis of polyketide synthase genes in the biocontrol fungus Clonostachys rosea. Sci Rep 8:1–17. https://doi.org/10.1038/s41598-018-33391-1

    Article  CAS  Google Scholar 

  116. Vergine M, Nicolì F, Sabella E et al (2020) Secondary Metabolites in Xylella fastidiosa—Plant Interaction. Pathogens 9:675

    Article  CAS  PubMed Central  Google Scholar 

  117. Shah N, Li Q, Xu Q et al (2020) CRb and PbBa8. 1 synergically increases resistant genes expression upon infection of Plasmodiophora brassicae in Brassica napus. Genes (Basel) 11:202

    Article  CAS  Google Scholar 

  118. Li H, Zhou Z, Hua H, Ma W (2020) Comparative transcriptome analysis of defense response of rice to Nilaparvata lugens and Chilo suppressalis infestation. Int J Biol Macromol 163:2270–2285

    Article  CAS  PubMed  Google Scholar 

  119. Boba A, Kostyn K, Kozak B et al (2020) Fusarium oxysporum infection activates the plastidial branch of the terpenoid biosynthesis pathway in flax, leading to increased ABA synthesis. Planta 251:1–14

    Article  CAS  Google Scholar 

  120. Ghosh S, Malukani KK, Chandan RK et al (2019) How plants respond to pathogen attack: interaction and communication. In: Sensory biology of plants. Springer, pp 537–568

    Chapter  Google Scholar 

  121. Ceulemans E, Ibrahim HMM, De Coninck B, Goossens A (2021) Pathogen effectors: exploiting the promiscuity of plant signaling hubs. Trends Plant Sci 26(8):780–795

    Article  CAS  PubMed  Google Scholar 

  122. Martel A, Ruiz-Bedoya T, Breit-McNally C et al (2021) The ETS-ETI cycle: evolutionary processes and metapopulation dynamics driving the diversification of pathogen effectors and host immune factors. Curr Opin Plant Biol 62:102011

    Article  CAS  PubMed  Google Scholar 

  123. Levin E, Kishore A, Ballester AR et al (2019) Identification of pathogenicity-related genes and the role of a subtilisin-related peptidase S8 (PePRT) in autophagy and virulence of Penicillium expansum on apples. Postharvest Biol Technol 149:209–220

    Article  CAS  Google Scholar 

  124. Liao Y, Shi W (2019) Read trimming is not required for map** and quantification of RNA-seq reads. NAR Genom Bioinform 2(3):lqaa068

    Article  CAS  Google Scholar 

  125. Grabherr MG, Haas BJ, Yassour M et al (2011) Trinity: reconstructing a full-length transcriptome without a genome from RNA-Seq data. Nat Biotechnol 29:644

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  126. Tegli S, Bini L, Calamai S et al (2020) A MATE Transporter is Involved in Pathogenicity and IAA Homeostasis in the Hyperplastic Plant Pathogen Pseudomonas savastanoi pv. nerii. Microorganisms 8:156

    Article  CAS  PubMed Central  Google Scholar 

  127. Thakur K, Chawla V, Bhatti S et al (2013) De novo transcriptome sequencing and analysis for Venturia inaequalis, the devastating apple scab pathogen. PLoS One 8:e53937

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  128. Zhao C, Waalwijk C, De Wit PJGM et al (2011) EBR1, a novel Zn2Cys6 transcription factor, affects virulence and apical dominance of the hyphal tip in Fusarium graminearum. Mol Plant-Microbe Interact 24:1407–1418. https://doi.org/10.1094/MPMI-06-11-0158

    Article  CAS  PubMed  Google Scholar 

  129. Oh M, Son H, Choi GJ et al (2016) Transcription factor ART1 mediates starch hydrolysis and mycotoxin production in Fusarium graminearum and F.verticillioides. Mol Plant Pathol 17(5):755–768. https://doi.org/10.1111/mpp.12328

    Article  CAS  PubMed  Google Scholar 

  130. Brown DW, Lee SH, Kim LH et al (2015) Identification of a 12-gene fusaric acid biosynthetic gene cluster in Fusarium species through comparative and functional genomics. Mol Plant-Microbe Interact 28:319–332. https://doi.org/10.1094/MPMI-09-14-0264-R

    Article  CAS  PubMed  Google Scholar 

  131. Urban M, Cuzick A, Rutherford K et al (2017) PHI-base: a new interface and further additions for the multi-species pathogen-host interactions database. Nucleic Acids Res 45:604–610. https://doi.org/10.1093/nar/gkw1089

    Article  CAS  Google Scholar 

  132. Hugot K, Riviere M-P, Moreilhon C et al (2004) Coordinated regulation of genes for secretion in tobacco at late developmental stages: association with resistance against oomycetes. Plant Physiol 134:858–870

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  133. Niderman T, Genetet I, Bruyere T et al (1995) Pathogenesis-related PR-1 proteins are antifungal (isolation and characterization of three 14-kilodalton proteins of tomato and of a basic PR-1 of tobacco with inhibitory activity against Phytophthora infestans). Plant Physiol 108:17–27

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  134. Chittoor JM, Leach JE, White FF (1997) Differential induction of a peroxidase gene family during infection of rice by Xanthomonas oryzae pv. oryzae. Mol Plant-Microbe Interact 10:861–871

    Article  CAS  PubMed  Google Scholar 

  135. Passardi F, Cosio C, Penel C, Dunand C (2005) Peroxidases have more functions than a Swiss army knife. Plant Cell Rep 24:255–265

    Article  CAS  PubMed  Google Scholar 

  136. Van Loon LC (1997) Induced resistance in plants and the role of pathogenesis-related proteins. Eur J Plant Pathol 103:753–765

    Article  Google Scholar 

  137. Lane BG (2002) Oxalate, germins, and higher-plant pathogens. IUBMB Life 53:67–75

    Article  CAS  PubMed  Google Scholar 

  138. Lou Y, Baldwin IT (2006) Silencing of a germin-like gene in Nicotiana attenuata improves performance of native herbivores. Plant Physiol 140:1126–1136

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  139. Godfrey D, Able AJ, Dry IB (2007) Induction of a grapevine germin-like protein (VvGLP3) gene is closely linked to the site of Erysiphe necator infection: a possible role in defense? Mol Plant-Microbe Interact 20:1112–1125

    Article  CAS  PubMed  Google Scholar 

  140. Guevara-Olvera L, Ruíz-Nito ML, Rangel-Cano RM et al (2012) Expression of a germin-like protein gene (CchGLP) from a geminivirus-resistant pepper (Capsicum chinense Jacq.) enhances tolerance to geminivirus infection in transgenic tobacco. Physiol Mol Plant Pathol 78:45–50

    Article  CAS  Google Scholar 

  141. Pedley KF, Martin GB (2005) Role of mitogen-activated protein kinases in plant immunity. Curr Opin Plant Biol 8:541–547

    Article  CAS  PubMed  Google Scholar 

  142. Edgar R (2016) UNOISE2: improved error-correction for Illumina 16S and ITS amplicon sequencing. bioRxiv. https://doi.org/10.1101/081257

  143. Amir A, McDonald D, Navas-Molina JA et al (2017) Deblur rapidly resolves single-nucleotide community sequence patterns. mSystems 2(2):e00191–e00116. https://doi.org/10.1128/msystems.00191-16

    Article  PubMed  PubMed Central  Google Scholar 

  144. Schloss PD, Westcott SL, Ryabin T et al (2009) Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol 75(23):7537–7541. https://doi.org/10.1128/AEM.01541-09

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  145. Durling MB, Clemmensen KE, Stenlid J, Lindahl B (2011) SCATA-An efficient bioinformatic pipeline for species identification and quantification after high-throughput sequencing of tagged amplicons

    Google Scholar 

  146. Rognes T, Flouri T, Nichols B et al (2016) VSEARCH: a versatile open source tool for metagenomics. PeerJ 4:e2584. https://doi.org/10.7717/peerj.2584

    Article  PubMed  PubMed Central  Google Scholar 

  147. Pauvert C, Buée M, Laval V et al (2019) Bioinformatics matters: the accuracy of plant and soil fungal community data is highly dependent on the metabarcoding pipeline. Fungal Ecol 41:23–33. https://doi.org/10.1016/j.funeco.2019.03.005

    Article  Google Scholar 

  148. Weiss S, Xu ZZ, Peddada S et al (2017) Normalization and microbial differential abundance strategies depend upon data characteristics. Microbiome 5:1–18

    Article  Google Scholar 

  149. Willis AD (2019) Rarefaction, alpha diversity, and statistics. Front Microbiol 10:2407

    Article  PubMed  PubMed Central  Google Scholar 

  150. Pereira MB, Wallroth M, Jonsson V, Kristiansson E (2018) Comparison of normalization methods for the analysis of metagenomic gene abundance data. BMC Genomics 19:1–17

    Article  CAS  Google Scholar 

  151. Beule L, Karlovsky P (2020) Improved normalization of species count data in ecology by scaling with ranked subsampling (SRS): application to microbial communities. PeerJ 8:e9593

    Article  PubMed  PubMed Central  Google Scholar 

  152. Robinson MD, Oshlack A (2010) A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol 11:1–9

    Article  Google Scholar 

  153. Anders S, Huber W (2010) Differential expression analysis for sequence count data. Nat Preced 1

    Google Scholar 

  154. Paulson JN, Colin Stine O, Bravo HC, Pop M (2013) Differential abundance analysis for microbial marker-gene surveys. Nat Methods 10:1200–1202. https://doi.org/10.1038/nmeth.2658

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  155. Spellerberg IF, Fedor PJ (2003) A tribute to Claude Shannon (1916–2001) and a plea for more rigorous use of species richness, species diversity and the ‘Shannon–Wiener’ Index. Glob Ecol Biogeogr 12:177–179

    Article  Google Scholar 

  156. Liu J, Abdelfattah A, Norelli J et al (2018) Apple endophytic microbiota of different rootstock/scion combinations suggests a genotype-specific influence. Microbiome 6:1–11

    Article  Google Scholar 

  157. Abdelfattah A, Sanzani SM, Wisniewski M et al (2019) Revealing cues for fungal interplay in the plant–air interface in vineyards. Front Plant Sci 10:922. https://doi.org/10.3389/fpls.2019.00922

    Article  PubMed  PubMed Central  Google Scholar 

  158. Piombo E, Abdelfattah A, Danino Y et al (2020) Characterizing the fungal microbiome in date (Phoenix dactylifera) fruit pulp and peel from early development to harvest. Microorganisms 8:641

    Article  CAS  PubMed Central  Google Scholar 

  159. Schoch CL, Seifert KA, Huhndorf S et al (2012) Nuclear ribosomal internal transcribed spacer (ITS) region as a universal DNA barcode marker for Fungi. Proc Natl Acad Sci U S A 109(16):6241–6246. https://doi.org/10.1073/pnas.1117018109

    Article  PubMed  PubMed Central  Google Scholar 

  160. Stielow JB, Lévesque CA, Seifert KA et al (2015) One fungus, which genes? Development and assessment of universal primers for potential secondary fungal DNA barcodes. Persoonia 35:242–263. https://doi.org/10.3767/003158515X689135

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  161. Samson RA, Visagie CM, Houbraken J et al (2014) Phylogeny, identification and nomenclature of the genus Aspergillus. Stud Mycol 53(1):1–27. https://doi.org/10.1016/j.simyco.2014.07.004

    Article  Google Scholar 

  162. Abdelfattah A, Malacrinò A, Wisniewski M et al (2018) Metabarcoding: a powerful tool to investigate microbial communities and shape future plant protection strategies. Biol Control 120:1–10. https://doi.org/10.1016/j.biocontrol.2017.07.009

    Article  Google Scholar 

  163. Bagga S, Bracht J, Hunter S et al (2005) Regulation by let-7 and lin-4 miRNAs results in target mRNA degradation. Cell 122:553–563

    Article  CAS  PubMed  Google Scholar 

  164. Gu K, Mok L, Chong MMW (2018) Regulating gene expression in animals through RNA endonucleolytic cleavage. Heliyon 4:e00908

    Article  PubMed  PubMed Central  Google Scholar 

  165. Brodersen P, Sakvarelidze-Achard L, Bruun-Rasmussen M et al (2008) Widespread translational inhibition by plant miRNAs and siRNAs. Science 320:1185–1190

    Article  CAS  PubMed  Google Scholar 

  166. Djuranovic S, Nahvi A, Green R (2012) miRNA-mediated gene silencing by translational repression followed by mRNA deadenylation and decay. Science 336:237–240

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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  1. Department of Forest Mycology and Plant Pathology, Uppsala Biocenter, Swedish University of Agricultural Sciences, Uppsala, Sweden

    Edoardo Piombo & Mukesh Dubey

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  1. Edoardo Piombo
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  2. Mukesh Dubey
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  1. Institute for Sustainable Plant Protection, National Research Council (IPSP-CNR), Sesto Fiorentino, Florence, Italy

    Nicola Luchi

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Piombo, E., Dubey, M. (2022). Computational Analysis of HTS Data and Its Application in Plant Pathology. In: Luchi, N. (eds) Plant Pathology. Methods in Molecular Biology, vol 2536. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2517-0_17

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