Abstract
Bioinformatics is the study of molecular biological data using techniques of computer sciences and statistical analysis to solve the biological problem. The main task of bioinformatics is to store, process and analyse the huge biological data obtained from the experimental research. Similarly, plant pathology involves the study of the basis of plant disease resistance, identification of the pathogens, disease aetiology, disease cycles, genetics of pathology and management of plant diseases. Therefore, to understand the molecular mechanism of pathogenesis of plant pathogens is a major aspect of plant biology. So various bioinformatics-based methods and tools have been developed for comparative genomic analysis, evolutionary analysis, genome-wide association study (GWAS), molecular modelling methods and so on. The specific analysis includes illustrating the mechanism behind the plant-pathogen interaction and predictions of the accurate location of the disease-causing genes on the genome that lead to the development of the disease-free transgenic plant. Comparative genomics of plant pathogens of an emerging crop is one of the effective approaches of bioinformatics-based analysis. Similarly, the development of unique user-friendly bioinformatics database resources of different aspects of plant pathology will facilitate the sharing of information among the scientific communities and will ultimately be beneficial to plant breeders and farmers. This chapter focuses on the recent challenges and opportunities in plant pathology research by narrating the literature and how the bioinformatics methods have been used to solve the problems.
Access provided by Autonomous University of Puebla. Download chapter PDF
Similar content being viewed by others
Keywords
11.1 Introduction
Plants are the major sources of food, fibre and fuel in the agriculture sector and hence play a dominant role in the world economy. Plant pathogens cause a major threat to and are responsible for the huge loss in crops by causing diseases in plants. Also, plant pathogens spread very quickly while infecting a healthy plant from a diseased plant. So the primary challenge associated with a plant pathologist is to minimise crop loss by eradiating the plant pathogen (Mack et al. 2000; Mitra 2021). Plants are persistently under the threat of several pathogens like bacteria, viruses, fungi, nematodes and others. However, molecular complicacy in plant-pathogen interactions makes it difficult to interpret. The plant pathogens that cause disease in plants are directly responsible for food security and scarcity and ultimately even threaten human health. However, plants also contain a specific immune system that provides resistance to the pathogen. Plants have evolved highly sophisticated mechanisms to resist pathogens by using different barriers and induction of specific signalling pathways. The induction of several metabolic pathways in the plant system also requires the recognition of the pathogen by pathogen-derived factors and by specific proteins (effector molecules) that are encoded by pathogens. However, if the pathogen is suppressed, these factors enable them to infect and cause diseases in plants. Due to recent developments in the fundamental biological research, many of the interesting molecular mechanisms regarding infection of pathogen, effector molecule and modulator activity of the immune systems are known (Kachroo et al. 2017; Zhang et al. 2013; de Wit 2007). In addition to the fundamental molecular biological research, due to the advancement of genomic technologies, there is a flooding of a huge amount of genomic information for the analysis by in silico methods. However, challenges exist to validate the biological data as well as for proper prediction and interpretation. In this aspect, bioinformatics-based analysis plays a major role in the management of data. Bioinformatics is generally defined as the application of computational techniques to the storing, processing and managing of the biological data that are usually generated from molecular biological experiments. The ultimate objective of bioinformatics is the functional and statistically reliable prediction from the given biological data. To facilitate this, several categories of databases, web servers as well as executable software are being developed and many are currently available with a suitable interface for analysis and interpretation of these data. Bioinformatics-based analysis facilitates to open the door to understand the complex biological processes by implementing the genomic and protein sequence analysis, advanced data mining and machine learning algorithms on biological data and molecules (Fig. 11.1). So, the new knowledge can be suitably used for several aspects of biotechnological research (Untergasser et al. 2007; Mishra et al. 2016; Singh et al. 2011; Satpathy 2014; Satpathy et al. 2015).
The challenge in controlling plant diseases lies upon the molecular basis of identification of the key pathogenic factors that are responsible for spreading in case of a specific plant pathogen. Many of these molecular pieces of information are available that can be suitably analysed by in silico methods. This chapter provides a specific report on the application of specific computational tools and the methods for plant pathogen analysis such as the study of host-pathogen interaction, molecular modelling studies and whole-genome analysis (WGA) used for plant pathology research (Alemu 2015).
11.2 Applications of Bioinformatics in the Plant Pathological Study
Biological databases are a repository of several molecular biological data that are stored in a consistent manner. For example, the database might contain a single file containing several records but each of which having the same set of information. Similar several tools are available to understand the mechanism and function of metabolites and compounds and their pathway details involved in the phytopathological mechanism. Plants having the potential to resists themselves from the infection of a pathogen are known as resistant plants and in this case the host-pathogen interaction is considered as incompatible. Despite the economic impact of plant pathology, the fundamental molecular mechanisms underlying the pathogenicity of pathogens are still poorly understood, which opens the door for implementation of bioinformatics methods (Andersen et al. 2018; Scholthof 2001; Narayanasamy 2008).
From the biological point of view the in-depth study of plant pathogenesis processes includes four different approaches: (a) gene expression analysis, (b) structural and comparative genomics, (c) molecular modelling study and (d) GWAS analysis. Currently, the databases contain many numbers of molecular data of host plants, as well as the information of plant pathological aspects of specific pathogens provides a strong platform to analyse the data (Fig. 11.2). Many of the databases and tools have been developed to perform a thorough analysis specifically in the plant pathology area (Tables 11.1 and 11.2).
Some of the specific implementations of the bioinformatics applications for the plant pathology study are described under the following sections:
11.2.1 Plant-Pathogen Interaction Study
Plant-pathogen interactions exhibit several important molecular responses based on which pathogens can colonise and spreading of the disease occurs. For example, some fungi produce secondary metabolites that control a wide range of molecular functions such as the production of virulence factors siderophores and phytotoxins that lead to the establishment of the disease. Shi-Kunne et al. carried out in silico analysis to identify the 25 potential secondary metabolites producing gene clusters in case of Verticillium dahliae (Shi-Kunne et al. 2019). Graham-Taylor et al. described the number of gene clusters with a potential role in virulence in Sclerotinia sclerotiorum (Graham-Taylor et al. 2020). Computational analysis by Kamal et al. described the identification of interacting regions in Begomovirus-encoded βC1 protein with cotton plant (Gossypium hirsutum) SnRK1 protein by using computational approaches including sequence recognition, and binding site and interface prediction methods followed by experimental analysis (Kamal et al. 2019). Pavlopoulou described the interacting molecules that are involved in plant defence by building a protein-protein interaction (PPi) network, and provided evidence for prominent crosstalk between the various defence mechanisms to several stresses including pathogen infection (Pavlopoulou et al. 2019). Kaur et al. (2017) analysed the expression pattern and role of pathogenesis-related (PR) proteins (possess antifungal activities such as PR-1, PR-2, PR-5, PR-9, PR-10 and PR-12) in case of Arabidopsis thaliana and Oryza sativa by using computational analysis. The in silico study about the plant cell wall-degrading enzymes (PCWDEs) has been carried out by Chang et al. (2016). As plant pathogens secrete PCWDEs for the degradation of plant cell walls, to counter this, plants also release some PCWDE inhibitor proteins (PIPs) to reduce the infection. However, some of the species of the pathogen Fusarium can escape this PIP inhibition. So in silico study has been performed to understand this resistance mechanism by analysing the genomic structure of the pathogen.
11.2.2 Gene Expression, Structural and Comparative Genomics Study
Study about the expression pattern of pathogenesis-related genes is important to build the computational model of the establishment process of plant diseases. The gene expression analysis also leads to identifying the pathogenic genes and expression profile in different host systems. This finally provides insights into the possible ways of attack and resistance mechanisms involved in the pathogenesis process. In addition to this, comparative genomics about the different pathogens and among host and pathogens is essential to identify the region of the gene responsible for pathogenesis and resistance. Also, it is possible to explore the distribution of homologous genes and their locus in several pathogenic genomes. The gene expression pattern as well as structural and comparative genomics-based study uncovers the path to gain deeper knowledge about the relationship between the host plant and pathogens. Pinzón et al. studied the gene expression of Phytophthora infestans in host cells and identified the favourable and non-favourable patterns of gene interaction. Further sequence-level analysis about resistance genes has been proposed to identify virulence gene pathogens and gene families (Pinzón et al. 2009). Comparative genomics analysis by Klosterman et al. (2011) established a set of proteins that are shared among three selected fungal pathogens which cause the wilt disease. A homologue of a bacterial homologous gene glucosyltransferase that synthesises virulence-related osmoregulated periplasmic glucans to adopt the pathogen in osmotic stress has been identified. Valero-Jiménez et al. (2019) used comparative genomics methods to determine the function of 7668 protein families of selected 9 numbers of Botrytis species. These families of proteins were observed in two distinct phylogenetic clades that contain unique genes for secondary metabolite synthesis. Benevenuto et al. implemented the comparative genomics approach to analyse the genetic basis of invading the smut fungi that infect different host systems. Different types of genes such as positively selected genes, gain or loss of effector genes, orphan genes and a genomic signature have been studied in terms of their host specialisation (Benevenuto et al. 2018). Adhikari et al. (2013) reported the sequencing, assembly and annotation study of given six Pythium genomes with other plant pathogenic oomycetes such as Phytophthora species. The comparative genomic analysis established the close relationship between the oomycetes and Phytophthora species based on the involvement of different protein families with diverse functions. Different proteins such as proteolytic enzymes, effector molecules and cell wall-degrading enzymes were found to be associated according to the trophic behaviour of the pathogen. Trantas et al. conducted extensive comparative genomics of the pathogens Pseudomonas corrugata and Pseudomonas mediterranea to identify the gene clusters for the biosynthesis of siderophores and other metabolites (Trantas et al. 2015). Chen et al. (2019) studied the genomic assembly of Puccinia hordei (Ph), which is a damaging pathogen of barley, and identified three candidate genes that can be investigated further for their biological properties, to uncover the mechanism of pathogen virulence. Genomic analyses by Méndez et al. showed the phylogenetic relationships among three Chilean strains of Clavibacter and identified the unique virulence factors responsible for virulence activity in tomato plants (Méndez et al. 2020).
11.2.3 Molecular Modelling Study
Progress in computational molecular modelling studies in the last 20 years about plant-pathogen analysis has revealed some of the key mechanisms of this complex process. Due to the availability of the genomic and protein sequence information as well as the three-dimensional (3D) structures, it is possible to use several molecular modelling approaches to deduce basic molecular phenomena associated with it. Molecular modelling analysis depicts different processes such as the interaction of pathogen-secreted molecules with host target molecules followed by their responses. It is also essential to study different molecules and the metabolic pathways in the case of the plants that play an important role in establishing the diseases. Apart from this, the activity, affinity and specificity of specific agrochemicals towards the pathogenic target can be obtained by applying computer-aided drug design (CADD) methods in the plant pathology area. After choosing specific target molecules in the database such as Protein Data Bank (PDB), specific chemical molecules can be docked to identify the binding site, energy as well as position of chemicals by the process of molecular docking.
A review by Shanmugam and Jeon (2017) described two major categories of computer-based drug discovery strategies, such as structure-based drug design (SBDD) and ligand-based drug design (LBDD) as shown in Fig. 11.3. Several methods such as structure prediction, molecular docking, de novo ligand design, pharmacophore modelling and quantitative structure-activity relationship modelling are used to facilitate the drug design process as described in Fig. 11.3. Shanmugam et al. (2019) studied the essential enzyme such as MoRPD3, a histone deacetylase (HDAC), that causes histone protein acetylation and deacetylation, which helps in the growth and development of rice blast fungus, Magnaporthe oryzae. So considering the protein as the drug target to which several compounds were virtually screened by molecular docking method followed by in vitro study and 3D QSAR analysis suggested that [2-[[4-(2-methoxyethyl) phenoxy] methyl] phenyl] boronic acid compound is a good hit as a HDAC inhibitor. Kumar et al. (2020) used the molecular docking (protein-protein) method between the polygalacturonase inhibitor protein of banana and polygalacturonase (PG) of the pathogen Erwinia carotovora. Further, in silico site-directed mutagenesis, docking and molecular dynamics simulation results revealed that particularly the residues at the active sites and the structural changes are responsible for the inhibition of enzyme activity. System biological computational model has been utilised by Islam et al. (2020), who identified three potential antifungal compounds from Bacillus subtilis that can be suitably used for suppression of Rhizoctonia solani mycelium growth. In silico analysis was performed by using homology modelling and molecular docking followed by molecular dynamics simulation and ADMET analysis. Imran and Ravi (2020) predicted 3D structures of potential drug target proteins of the plant pathogen Colletotrichum falcatum that causes ‘red rot’ disease of sugar cane. This study was conducted by using online resources to construct homology models of drug target proteins against which the suitable drug molecule can be designed. Mishra et al. (2019) used virtual screening and molecular docking strategies to find the lead compounds against fungal diseases such as Fusarium wilt, rice blast, late blight of potato, necrotrophic, early blight of Solanaceae members, flax rust to eradicate these. In the study, seven different antifungal ligand molecules were docked into the selected target proteins of six different fungal pathogens and it showed that several hydrophobic and polar contacts are responsible for binding of the ligand molecule. Pathak et al. (2016) considered molecular targets such as ABC transporter, Amr1, beta-tubulin, cutinase, fusicoccadiene synthase and glutathione transferase of Alternaria brassicicola in order to study the binding affinity with phytoalexin. Molecular modelling and docking confirmed that the compound spirobrassinin can be used for the protection of Brassica plants against infection by Alternaria sp. In the work by Prajapat et al. (2011) the homology modelling method was followed to deduce the 3D structure of coat protein of mimosa yellow vein virus. The subsequent molecular docking study was performed on the modelled structure of coat protein with α-lactalbumin and further binding pattern was analysed. A recent molecular modelling and protein-protein docking study of pepper yellow leaf curl virus (PepYLCV) pathogenicity protein BC1 and pepper SnRK1 protein revealed the involvement of domain-level interaction in pathogenicity (Nova and Jamsari 2020). The in silico approach for the domain arrangement study of several R-proteins belonging to 33 plant organisms was analysed by Sanseverino and Ercolano (2012). Detailed analysis performed on conserved profiles revealed that specific domain features and several atypical domain associations were also obtained from a diverse set of R-proteins.
11.2.4 GWAS Study in Plant Pathology
Genome-wide association studies (GWAS) are an effective tool widely used for mapping multiple traits in case of wild-type genome. The advancement in genomic sequencing technologies with reduced cost of genotyping, enhanced computational efficiency and development of improved algorithms has made the genome-wide association study more perfect to explore the position of several essential traits. The basic objective of the GWAS is to identify single-nucleotide polymorphisms (SNP) in the given population, so that any other trait can be measured that is associated with it. Hence, it is expected that such associations may provide variants in specific genes that play a crucial role in the phenotype of interest. Presently, this method is suitable for identifying important genes in natural populations and is being widely used in case of plants for traits as crop yield, crop quality, disease resistance and abiotic stress tolerance (Skøt et al. 2005; Quesada et al. 2010; Rosenberg et al. 2010). The basic steps followed for the GWAS analysis have been described by Marees et al. (2018) and are outlined as below:
DNA sample (from cases and controls) → Hybridise DNA to the array → Identify the genotypes → Find additional SNPs → Find the hotspot for the disease resistance gene → Compute the association of SNP markers with disease resistance genes → Perform statistical analysis → Interpret findings
Alqudah et al. (2020) conducted the genome-wide association study (GWAS) with the aim to map the stem rust resistance loci of barley plant genome by identifying single-nucleotide polymorphic (SNP) markers. Bartoli and Roux (2017) described the importance of GWA mapping tool for the detection of genomic regions associated with disease resistance that predicts the pathogenicity in plant pathogens. Shrestha et al. (2019) reviewed the implementation of GWAS analysis in five major disease resistance varieties of maize plant along with novel SNPs and identification of novel disease resistance genes associated with it. Sánchez-Vallet et al. (2018) used both GWAS and classic linkage mapping methods to establish the function of the avirulence effector of Zymoseptoria tritici that is recognised by the resistance genes of wheat. A GWAS study by Volante et al. (2017) identified two regions (qBK1_628091 of chromosome 1 and qBK4_31750955 of chromosome 4) in the genome of Oryza japonica rice plant that are associated with the single-nucleotide polymorphism (SNP) marker and proposed to be involved in bakanae disease resistance mechanism.
11.3 Future Aspects
Despite many advancements in research in the area of plant pathology, molecular basis of various functions is still poorly understood. Hence it becomes essential to study the complex mechanism using bioinformatics-based tools and methods. Some of the opportunities for the application of bioinformatics in plant pathology are as follows:
-
Exploring the phylogenetic as well as the structural basis for the study of biomolecules associated with the plant immune system and their distribution across taxonomical diverse species.
-
Analysing the plant pathological system and understanding the mechanism of resistance against virulence factors acquired by diverse host plants.
-
Understanding the structural features of specific plant proteins to predict the pathological phenomena like how pathogens cause disease in plants and how plants defend themselves against pathogens.
-
Development of a unique database of the plant pathogenic target is essential to discover the role of new agrochemicals as the effective drug molecule.
-
Use of system biological study by using the available multiomics data is an important aspect, in which it will enable to develop new sophisticated models relating to the phenomena like plant-pathogen-disease establishment-environmental factors/parameters.
-
The next-generation sequencing data from the database can be conveniently used for the analysis of plant genome and pathogens to elucidate the key genomic features associated with the pathogenesis.
11.4 Conclusion
Plant diseases cause significant destruction of crop plants ultimately leading to huge economic loss worldwide, especially in the food production sector. So it is crucial to study the disease-causing mechanism related to physiological systems in case of plants. Research on plant-pathogen as well as the molecular basis of the study is interesting as well as complex too while conducting experiment and interpreting the result. However in the recent age, implementation of bioinformatics-based application makes the prediction task easy. The availability of sophisticated software tools and databases for the biological information about the plant pathology enables researchers to focus on in silico studies of individual components in which genes and proteins can be investigated. In this chapter a recent view of different bioinformatics-based methods that are being used by researchers has been provided. In addition to this, major bioinformatics resources have been listed out that can be implemented to retrieve and analyse plant pathological data. However, in the upcoming years, one of the major challenges for the scientific community of plant pathology is more utilisation of the genomics data and tools in model plants so that it can be extrapolated to the disease management aspects. Ultimately this will lead to the enhancement of productivity. Therefore, bioinformatics-based findings would provide a deeper understanding and insights into plant pathogen-host protein interactions and will ultimately lead to understanding of the complex plant pathological system.
References
Adhikari BN, Hamilton JP, Zerillo MM et al (2013) Comparative genomics reveals insight into virulence strategies of plant pathogenic oomycetes. PLoS One 8(10):e75072. https://doi.org/10.1371/journal.pone.0075072
Alemu K (2015) The role and application of bioinformatics in plant disease management. Adv Life Sci Technol 28:28–33
Alqudah AM, Sallam A, Baenziger PS et al (2020) GWAS: fast-forwarding gene identification and characterization in temperate Cereals: lessons from Barley—a review. J Adv Res 22:119–135. https://doi.org/10.1016/j.jare.2019.10.013
Andersen EJ, Ali S, Byamukama E et al (2018) Disease resistance mechanisms in plants. Genes 9:339. https://doi.org/10.3390/genes9070339
Bartoli C, Roux F (2017) Genome-wide association studies in plant pathosystems: toward an ecological genomics approach. Front Plant Sci 8:763. https://doi.org/10.3389/fpls.2017.00763
Benevenuto J, Teixeira-Silva NS, Kuramae EE et al (2018) Comparative genomics of smut pathogens: insights from orphans and positively selected genes into host specialization. Front microbial 9:660. https://doi.org/10.3389/fmicb.2018.00660
Chang HX, Yendrek CR, Caetano-Anolles G et al (2016) Genomic characterization of plant cell wall degrading enzymes and in-silico analysis of xylanses and polygalacturonases of Fusarium virguliforme. BMC Microbiol 16(1):1–12. https://doi.org/10.1186/s12866-016-0761-0
Chen J, Wu J, Zhang P et al (2019) De novo genome assembly and comparative genomics of the barley leaf rust pathogen Puccinia hordei identifies candidates for three avirulence genes. G3 (Bethesda) 9:3263–3271
de Wit PJGM (2007) How plants recognize pathogens and defend themselves. Cell Mol Life Sci 64:2726–2732. https://doi.org/10.1007/s00018-007-7284-7
Graham-Taylor C, Kamphuis LG, Derbyshir MC (2020) A detailed in silico analysis of secondary metabolite biosynthesis clusters in the genome of the broad host range plant pathogenic fungus Sclerotinia sclerotiorum. BMC Genomics 21:1–20. https://doi.org/10.1186/s12864-019-6424-4
Imran S, Ravi L (2020) Elucidation of computational 3D models of protein drug targets for Colletotrichum falcatum a fungal plant pathogen causing red rod of sugarcane. Biomed Pharmacol J 13:627–633. https://doi.org/10.13005/bpj/1926
Islam MS, Mahmud S, Sultana R et al (2020) Identification and in silico molecular modelling study of newly isolated Bacillus subtilis SI-18 strain against S9 protein of Rhizoctonia solani. Arab J Chem 13:8600–8612. https://doi.org/10.1016/j.arabjc.2020.09.044
Kachroo A, Vincelli P, Kachroo P (2017) Signaling mechanisms underlying resistance responses: what have we learned, and how is it being applied? Phytopathology 107:1452–1461. https://doi.org/10.1094/PHYTO-04-17-0130-RVW
Kamal H, Minhas FUAA, Farooq M (2019) In silico prediction and validations of domains involved in Gossypium hirsutum SnRK1 protein interaction with cotton leaf curl Multan betasatellite encoded βC1. Front Plant Sci 10:656. https://doi.org/10.3389/fpls.2019.00656
Kaur A, Pati PK, Pati AM et al (2017) In-silico analysis of cis-acting regulatory elements of pathogenesis-related proteins of Arabidopsis thaliana and Oryza sativa. PLoS One 12:e0184523. https://doi.org/10.1371/journal.pone.0184523
Klosterman SJ, Subbarao KV, Kang S et al (2011) Comparative genomics yields insights into niche adaptation of plant vascular wilt pathogens. PLoS Pathog 7(7):e1002137. https://doi.org/10.1371/journal.ppat.1002137
Kumar S, Dehury B, Tandon G et al (2020) An insight into molecular interaction of PGIP with PG for banana cultivar. Front Biosci (Landmark Ed) 25:335–362. https://doi.org/10.2741/4809
Mack RN, Simberloff D, Lonsdale WM et al (2000) Biotic invasions: causes, epidemiology, global consequences, and control. Ecol Appl 10:689–710
Marees AT, de Kluiver H, Stringer S et al (2018) A tutorial on conducting genome-wide association studies: quality control and statistical analysis. Int J Methods Psychiatr Res 27(2):e1608. https://doi.org/10.1002/mpr.1608
Méndez V, Valenzuela M, Salvà-Serra F et al (2020) Comparative genomics of pathogenic Clavibacter michiganensis subsp. michiganensis strains from Chile reveals potential virulence features for tomato plants. Microorganisms 8(11):1679. https://doi.org/10.3390/microorganisms8111679
Mishra P, Eswaran M, Raman NM et al (2019) Probing of phytofungal proteins for fungicidal activity by molecular docking. J Proteomics Bioinform 12:079–084. https://doi.org/10.35248/0974-276X.19.12.499
Mishra VK, Mishra RR, Singh A et al (2016) Importance of bioinformatics for development of neglected and underutilized (Orphan) crops in India. Agri 5:20–29
Mitra D (2021) Emerging plant diseases: research status and challenges. In: Singh KP, Jahagirdar S, Sarma BK (eds) Emerging trends in plant pathology. Springer, Singapore, pp 1–17. https://doi.org/10.1007/978-981-15-6275-4_1
Narayanasamy P (2008) Molecular biology in plant pathogenesis and disease management: disease management, vol 3. Springer Science & Business Media
Nova B, Jamsari J (2020, April) In silico analysis of PepYLCV-βC1 protein interaction with pepper-SnRK1 for pathogenicity prediction. In: IOP conference series: earth and environmental science, vol 497, IOP Publishing, p 012027
Pathak RK, Taj G, Pandey D (2016) Molecular modeling and docking studies of phytoalexin (s) with pathogenic protein (s) as molecular targets for designing the derivatives with anti-fungal action on ‘Alternaria’ spp. of ‘Brassica’. Plant Omics 9:172–183. https://doi.org/10.21475/poj.16.09.03.p7654
Pavlopoulou A, Karaca E, Balestrazzi A et al (2019) In silico phylogenetic and structural analyses of plant endogenous danger signaling molecules upon stress. Oxidative Med Cell Longev 2019:8683054. https://doi.org/10.1155/2019/8683054
Pinzón A, Barreto E, Bernal A et al (2009) Computational models in plant-pathogen interactions: the case of Phytophthora infestans. Theor Biol Medical Model 6:1–11. https://doi.org/10.1186/1742-4682-6-24
Prajapat R, Marwal A, Sahu A et al (2011) Phylogenetics and in silico docking studies between coat protein of Mimosa yellow vein virus and whey α-lactalbumin. Am J Biochem Mol Biol 1:265–274
Quesada T, Gopal V, Cumbie WP et al (2010) Association mapping of quantitative disease resistance in a natural population of loblolly pine (Pinus taeda L.). Genetics 186:677–686
Rosenberg NA, Huang L, Jewett EM et al (2010) Genome-wide association studies in diverse populations. Nat Rev Genet 11:356–366
Sánchez-Vallet A, Hartmann FE, Marcel TC et al (2018) Nature’s genetic screens: using genome-wide association studies for effector discovery. Mol Plant Pathol 19:3–6. https://doi.org/10.1111/mpp.12592
Sanseverino W, Ercolano MR (2012) In silico approach to predict candidate R proteins and to define their domain architecture. BMC Res 5:1–11
Satpathy R, Konkimalla VB, Ratha J (2015) Application of bioinformatics tools and databases in microbial dehalogenation research: a review. Appl Biochem Microbiol 51:11–20. https://doi.org/10.7868/s0555109915010146
Satpathy R (2014) Bioinformatics resources for plant sciences. In: Biotechnology: an over view. Daya Publication, pp 53–65. ISBN: 978-93-5124-333-5
Scholthof HB (2001) Molecular plant-microbe interactions that cut the mustard. Plant Physiol 127:1476–1483
Shanmugam G, Jeon J (2017) Computer-aided drug discovery in plant pathology. Plant Pathol J 33:529–542
Shanmugam G, Kim T, Jeon J (2019) In silico identification of potential inhibitor against a fungal histone deacetylase, RPD3 from Magnaporthe Oryzae. Molecules 24(11):2075. https://doi.org/10.3390/molecules24112075
Shi-Kunne X, Jové RDP, Depotter JR et al (2019) In silico prediction and characterisation of secondary metabolite clusters in the plant pathogenic fungus Verticillium dahliae. FEMS Microbiol Lett 366:fnz081. https://doi.org/10.1093/femsle/fnz081
Shrestha V, Awale M, Karn A (2019) Genome Wide Association Study (GWAS) on disease resistance in Maize. In: Wani S (ed) Disease resistance in crop plants. Springer, Cham, pp 113–130. https://doi.org/10.1007/978-3-030-20728-1_6
Singh VK, Singh AK, Chand R et al (2011) Role of bioinformatics in agriculture and sustainable development. Int J Bioinform Res 3:221–226
Skøt L, Humphreys MO, Armstead I et al (2005) An association mapping approach to identify flowering time genes in natural populations of Lolium perenne (L.). Mol Breed 15:233–245
Trantas EA, Licciardello G, Almeida NF et al (2015) Comparative genomic analysis of multiple strains of two unusual plant pathogens: Pseudomonas corrugata and Pseudomonas mediterranea. Front Microbiol 6:811. https://doi.org/10.3389/fmicb.2015.00811
Untergasser A, Nijveen H, Rao X et al (2007) Primer3Plus, an enhanced web interface to Primer3. Nucleic Acids Res 35:W71–W74
Valero-Jiménez CA, Veloso J, Staats M et al (2019) Comparative genomics of plant pathogenic Botrytis species with distinct host specificity. BMC Genomics 20:1–12. https://doi.org/10.1186/s12864-019-5580-x
Volante A, Tondelli A, Aragona M et al (2017) Identification of bakanae disease resistance loci in japonica rice through genome wide association study. Rice (NY) 10(29):1–16. https://doi.org/10.1186/s12284-017-0168-z
Zhang Y, Lubberstedt T, Xu M (2013) The genetic and molecular basis of plant resistance to pathogens. J Genet Genomics 40:23–35. https://doi.org/10.1016/j.jgg.2012.11.003
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Satpathy, R. (2022). Application of Bioinformatics in the Plant Pathology Research. In: Nayak, S.K., Baliyarsingh, B., Singh, A., Mannazzu, I., Mishra, B.B. (eds) Advances in Agricultural and Industrial Microbiology. Springer, Singapore. https://doi.org/10.1007/978-981-16-9682-4_11
Download citation
DOI: https://doi.org/10.1007/978-981-16-9682-4_11
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-9681-7
Online ISBN: 978-981-16-9682-4
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)