Abstract
Over the past two decades, there have been significant advancements in the realm of transcriptomics, or the study of genes and their expression. Modern RNA sequencing technologies and high-performance computing are creating a “big data” revolution that provides new opportunities to explore the interactions between cereals and pathogens that affect grain yield and food safety. These data are being used to annotate genes and gene variants, as well as identify differentially expressed genes and create global gene co-expression networks. Moreover, these data can unravel the complex interactions between pathogen and host and identify genes and pathways involved in these interactions. This information can then be used for disease mitigation and the development of crops with superior resistance.
Bronwyn E. Rowland is the primary author to this chapter.
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References
Murray TD, Parry DW, Cattlin ND (2009) Diseases of small grain cereal crops: a colour handbook, Softcover edn. Manson Pub, London
Nilsen KT, Walkowiak S, Kumar S et al (2021) Histology and RNA sequencing provide insights into Fusarium head blight resistance in AAC Tenacious. Front Plant Sci 11:2114
McMullen M, Bergstrom G, De Wolf E et al (2012) A unified effort to fight an enemy of wheat and barley: Fusarium head blight. Plant Dis 96:1712–1728
Cole MB, Augustin MA, Robertson MJ, Manners JM (2018) The science of food security. NPJ Sci Food 2:14
Raza A, Razzaq A, Mehmood SS et al (2019) Impact of climate change on crops adaptation and strategies to tackle its outcome: a review. Plants 8:34
Cavicchioli R, Ripple WJ, Timmis KN et al (2019) Scientists’ warning to humanity: microorganisms and climate change. Nat Rev Microbiol 17:569–586
Garrett KA, Nita M, De Wolf ED et al (2021) Chapter 24: Plant pathogens as indicators of climate change. In: Letcher TM (ed) Climate change, 3rd edn. Elsevier, pp 499–513
Jones JDG, Dangl JL (2006) The plant immune system. Nature 444:323–329
Walkowiak S, Gao L, Monat C et al (2020) Multiple wheat genomes reveal global variation in modern breeding. Nature 588:277–283
The International Wheat Genome Sequencing Consortium (2018) Shifting the limits in wheat research and breeding using a fully annotated reference genome. Science 361:eaar7191
Ramírez-González RH, Borrill P, Lang D et al (2018) The transcriptional landscape of polyploid wheat. Science 361(6403):eaar6089
Walkowiak S, Bonner CT, Wang L et al (2015) Intraspecies interaction of Fusarium graminearum contributes to reduced toxin production and virulence. Mol Plant-Microbe Interact 28:1256–1267
Walkowiak S, Rowland O, Rodrigue N, Subramaniam R (2016) Whole genome sequencing and comparative genomics of closely related Fusarium Head Blight fungi: Fusarium graminearum, F. meridionale and F. asiaticum. BMC Genomics 17:1014
Puri KD, Yan C, Leng Y, Zhong S (2016) RNA-seq revealed differences in transcriptomes between 3ADON and 15ADON populations of Fusarium graminearum in vitro and in planta. PLoS One 11:e0163803
Giani AM, Gallo GR, Gianfranceschi L, Formenti G (2020) Long walk to genomics: history and current approaches to genome sequencing and assembly. Comp Struct Biotechnol J 18:9–19
Lenoir T, Giannella E (2006) The emergence and diffusion of DNA microarray technology. J Biomed Discov Collab 1:11–11
Weirauch MT (2011) Gene coexpression networks for the analysis of DNA microarray data. Applied Statistics for Network Biology, pp 215–250
Schena M (1996) Genome analysis with gene expression microarrays. BioEssays 18:427–431
Hon T, Mars K, Young G et al (2020) Highly accurate long-read HiFi sequencing data for five complex genomes. Sci Data 7:399
Johnson MTJ, Carpenter EJ, Tian Z et al (2012) Evaluating methods for isolating total RNA and predicting the success of sequencing phylogenetically diverse plant transcriptomes. PLoS One 7:e50226
Giardine B, Riemer C, Hardison RC et al (2005) Galaxy: a platform for interactive large-scale genome analysis. Genome Res 5:1451–1455
Conesa A, Madrigal P, Tarazona S et al (2016) A survey of best practices for RNA-seq data analysis. Genome Biol 17:13
Bolger AM, Lohse M, Usadel B (2014) Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30:2114–2120
Dobin A, Davis CA, Schlesinger F et al (2013) STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29:15–21
Kim D, Paggi JM, Park C et al (2019) Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat Biotechnol 37:907–915
Li H (2018) Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics 34:3094–3100
Wu TD, Watanabe CK (2005) GMAP: a genomic mapping and alignment program for mRNA and EST sequences. Bioinformatics 21:1859–1875
Besemer J, Lomsadze A, Borodovsky M (2001) GeneMarkS: a self-training method for prediction of gene starts in microbial genomes. Implications for finding sequence motifs in regulatory regions. Nucleic Acids Res 29:2607–2618
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
Kovaka S, Zimin AV, Pertea GM et al (2019) Transcriptome assembly from long-read RNA-seq alignments with StringTie2. Genome Biol 20:278
Trapnell C, Roberts A, Goff L et al (2012) Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat Protocols 7:562–578
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 Protocols 8:1494–1512
Keilwagen J, Hartung F, Grau J (2019) GeMoMa: homology-based gene prediction utilizing intron position conservation and RNA-seq data. Methods Mol Biol (Clifton, NJ) 1962:161–177
Hunter S, Apweiler R, Attwood TK et al (2008) InterPro: the integrative protein signature database. Nucleic Acids Res 37:D211–D215
Yao Z, You FM, N’Diaye A et al (2020) Evaluation of variant calling tools for large plant genome re-sequencing. BMC Bioinform 21:360
Cingolani P, Platts A, Wang LL et al (2012) A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff. Fly 6:80–92
Haile JK, N’Diaye A, Walkowiak S et al (2019) Fusarium head blight in durum wheat: recent status, breeding directions, and future research prospects. Phytopathology 109:1664–1675
Jin M, Liu H, He C et al (2016) Maize pan-transcriptome provides novel insights into genome complexity and quantitative trait variation. Sci Rep 6:18936
Jupe F, Witek K, Verweij W et al (2013) Resistance gene enrichment sequencing (RenSeq) enables reannotation of the NB-LRR gene family from sequenced plant genomes and rapid mapping of resistance loci in segregating populations. Plant J 76:530–544
Liao Y, Smyth GK, Shi W (2013) featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30:923–930
Anders S, Pyl PT, Huber W (2015) HTSeq – a Python framework to work with high-throughput sequencing data. Bioinformatics 31:166–169
Lawrence M, Huber W, Pagès H et al (2013) Software for computing and annotating genomic ranges. PLoS Comput Biol 9:e1003118
Robinson MD, McCarthy DJ, Smyth GK (2010) edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26:139–140
Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15:550
Shostak K, Bonner C, Sproule A et al (2020) Activation of biosynthetic gene clusters by the global transcriptional regulator TRI6 in Fusarium graminearum. Mol Microbiol 114:664–680
Fauteux F, Wang Y, Rocheleau H et al (2019) Characterization of QTL and eQTL controlling early Fusarium graminearum infection and deoxynivalenol levels in a Wuhan 1 x Nyubai doubled haploid wheat population. BMC Plant Biol 19:536
Bolouri H (2014) Modeling genomic regulatory networks with big data. Trends Genet 30:182–191
Yu H, Huang J, Zhang W, Han J-DJ (2011) Network analysis to interpret complex phenotypes. Applied Statistics for Network Biology, pp 1–12
Langfelder P, Horvath S (2008) WGCNA: an R package for weighted correlation network analysis. BMC Bioinform 9:559
Zhang J, Huang K (2014) Normalized ImQCM: an algorithm for detecting weak quasi-cliques in weighted graph with applications in gene co-expression module discovery in cancers. Cancer Inform 13s3:CIN.S14021
Grimes T, Potter SS, Datta S (2019) Integrating gene regulatory pathways into differential network analysis of gene expression data. Sci Rep 9:5479
Shannon P, Markiel A, Ozier O et al (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13:2498–2504
Zhang M, Li Q, Yu D et al (2019) GeNeCK: a web server for gene network construction and visualization. BMC Bioinform 20:12
Jeong H, Mason SP, Barabási AL, Oltvai ZN (2001) Lethality and centrality in protein networks. Nature 411:41–42
Barabási A-L, Oltvai ZN (2004) Network biology: understanding the cell’s functional organization. Nat Rev Genet 5:101–113
Farahbod M (2019) The interpretation of gene coexpression in systems biology. Doctoral dissertation, University of British Columbia. https://doi.org/10.14288/1.0387518
Li H, Sun Y, Zhan M (2009) Exploring pathways from gene co-expression to network dynamics. Methods Mol Biol (Clifton, NJ) 541:249–267
Sari E, Cabral AL, Polley B et al (2019) Weighted gene co-expression network analysis unveils gene networks associated with the Fusarium head blight resistance in tetraploid wheat. BMC Genomics 20:925
Gardiner DM, Kazan K, Manners JM (2009) Novel genes of Fusarium graminearum that negatively regulate deoxynivalenol production and virulence. Mol Plant-Microbe Interact 22:1588–1600
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© 2023 His Majesty the King in Right of Canada, as represented by the Minister of Agriculture and Agri-Food
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Rowland, B.E., Henriquez, M.A., Nilsen, K.T., Subramaniam, R., Walkowiak, S. (2023). Unraveling Plant-Pathogen Interactions in Cereals Using RNA-seq. In: Foroud, N.A., Neilson, J.A.D. (eds) Plant-Pathogen Interactions. Methods in Molecular Biology, vol 2659. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3159-1_9
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DOI: https://doi.org/10.1007/978-1-0716-3159-1_9
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