Bioscience Methods 2024, Vol.15, No.3, 102-113 http://bioscipublisher.com/index.php/bm 104 are activated or suppressed in response to pathogen attack, thereby elucidating the pathways involved in plant defense and pathogen virulence (Wise et al., 2007; Lowe et al., 2017). The advent of high-throughput sequencing technologies has significantly advanced our ability to perform comprehensive transcriptomic analyses, making it possible to study complex interactions at a genome-wide scale (Lee et al., 2018; Tyagi et al., 2022). 3.2 High-throughput sequencing technologies High-throughput sequencing technologies, particularly RNA sequencing (RNA-Seq), have revolutionized transcriptomic studies. RNA-Seq provides a high-resolution, sensitive, and quantitative method for analyzing the transcriptome, enabling the detection of both known and novel transcripts, including those expressed at low levels (Zhang et al., 2010; Tyagi et al., 2022). This technology has been instrumental in uncovering the complexity of the rice transcriptome, revealing extensive alternative splicing events and novel transcripts that were previously undetected (Zhang et al., 2010). RNA-Seq has also been applied to study the transcriptomes of both the rice plant and its pathogens, such as the rice blast fungus Magnaporthe oryzae, providing valuable insights into the dynamic interactions during infection (Jeon et al., 2020). The ability to simultaneously capture the transcriptomes of both host and pathogen through dual RNA-Seq further enhances our understanding of these interactions (Westermann et al., 2017). 3.3 Bioinformatics tools for transcriptomic data analysis The analysis of transcriptomic data requires sophisticated bioinformatics tools to process and interpret the vast amounts of data generated by high-throughput sequencing. These tools are essential for tasks such as read alignment, transcript assembly, differential expression analysis, and functional annotation (Lowe et al., 2017). Advances in bioinformatics have enabled more accurate and comprehensive analyses, facilitating the identification of key regulatory genes and pathways involved in plant-pathogen interactions (Lee et al., 2018). For instance, bioinformatics tools have been used to analyze the transcriptomes of rice and its pathogens, leading to the discovery of novel genes and regulatory mechanisms that play crucial roles in disease resistance and susceptibility (Wise et al., 2007; Jeon et al., 2020). 3.4 Functional annotation and gene ontology (GO) analysis in rice-pathogen studies Functional annotation and Gene Ontology (GO) analysis are critical steps in interpreting transcriptomic data. These approaches help to categorize genes into functional groups based on their biological processes, molecular functions, and cellular components, providing a clearer understanding of the roles they play in plant-pathogen interactions (Lowe et al., 2017; Tyagi et al., 2022). In rice-pathogen studies, GO analysis has been used to identify key functional categories and pathways that are differentially regulated during infection, shedding light on the molecular mechanisms of plant defense and pathogen virulence (Jeon et al., 2020). For example, transcriptomic studies have revealed that genes involved in defense response, signal transduction, and secondary metabolism are often upregulated in rice in response to pathogen attack, highlighting their importance in the plant's immune response (Zhang et al., 2010). 4 Case Study 4.1 Selection criteria for the case study The selection of case studies for this research on transcriptomic approaches to studying rice pathogen interactions was based on several key criteria. These criteria were designed to ensure that the selected studies provide comprehensive and high-quality data on the interactions between rice and its pathogens, particularly focusing on transcriptomic analyses. The primary criterion was the relevance of the study to the interactions between rice (Oryza sativa) and its pathogens. Studies that provided insights into the molecular and genetic mechanisms underlying these interactions were prioritized. For instance, the study by (Jeon et al., 2020) focused on the transcriptome profiling of the rice blast fungus Magnaporthe oryzae and its host during infection, providing valuable data on the expression profiles of both the pathogen and the host.
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