Bioscience Methods 2024, Vol.15, No.3, 102-113 http://bioscipublisher.com/index.php/bm 108 shown to play a significant role in defense responses, with several WRKY genes being differentially regulated upon pathogen attack (Ryu et al., 2006). 5.3 Role of non-coding RNAs in rice-pathogen interactions Non-coding RNAs, particularly long non-coding RNAs (lncRNAs), have emerged as important regulators in rice-pathogen interactions. A transcriptome analysis of rice infected with Rice black-streaked dwarf virus (RBSDV) identified 22 differentially expressed lncRNAs. These lncRNAs were found to co-express with 56 differentially expressed mRNAs involved in the plant-pathogen interaction pathway, suggesting their essential roles in rice innate immunity (Zhang et al., 2020). Furthermore, microRNAs (miRNAs) have also been implicated in these interactions, with specific miRNAs being up-regulated during infection, indicating their regulatory roles in defense mechanisms (Mahesh et al., 2020). 5.4 Comparative transcriptomics in different rice varieties Comparative transcriptomic studies across different rice varieties have provided insights into the genetic basis of resistance and susceptibility. For example, a meta-analysis of microarray data for rice infected by viruses from the Reoviridae and Sequiviridae families identified shared and divergent gene co-expression profiles. This study revealed four highly preserved gene modules and 83 common transcription factors targeting hub genes, highlighting the conserved and unique aspects of gene expression in response to different viral infections (Sahu et al., 2019). Additionally, transcriptome analysis of rice infected with different races of the blast fungus has shown that the timing and levels of gene expression can determine race-specific resistance (Haiyan et al., 2007). 6 Challenges and Limitations 6.1 Technical challenges in transcriptomic studies Transcriptomic studies in rice-pathogen interactions face several technical challenges. One significant issue is the difficulty in obtaining high-quality in planta transcriptome data during infection. For instance, previous studies on the rice blast fungus Magnaporthe oryzae and its host Oryza sativa have struggled with technical difficulties, resulting in suboptimal fungal transcriptome data (Jeon et al., 2020). Additionally, the complexity of the rice transcriptome, which includes a high number of novel transcripts, exons, and untranslated regions, further complicates the analysis (Zhang et al., 2010). The need for high-throughput and precise techniques, such as microdissection-based RNA sequencing, is essential to overcome these challenges and achieve comprehensive expression profiling (Jeon et al., 2020). 6.2 Limitations of current bioinformatics tools The current bioinformatics tools used for transcriptomic analysis have limitations that hinder the full understanding of rice-pathogen interactions. For example, while RNA sequencing (RNA-seq) has advanced our ability to map and quantify the transcriptome, the functional complexity of the rice transcriptome is still not fully elucidated (Lu et al., 2010). Many novel transcriptional active regions (nTARs) identified through RNA-seq lack homologs in public protein data, making functional annotation challenging (Lu et al., 2010). Moreover, the high percentage of alternative splicing events in rice genes adds another layer of complexity that current bioinformatics tools are not fully equipped to handle (Zhang et al., 2010). 6.3 Data interpretation and biological relevance Interpreting the vast amount of data generated from transcriptomic studies and determining its biological relevance is a significant challenge. The complexity of transcriptional regulation in rice, including alternative splicing and trans-splicing events, makes it difficult to draw clear conclusions about gene function and expression (Zhang et al., 2010). Additionally, the differential expression patterns observed in various studies need to be carefully analyzed to understand their implications in the context of host-pathogen interactions (Lu et al., 2010). The integration of transcriptomic data with other genomic and proteomic data is crucial for a more comprehensive understanding but remains a challenging task (Wise et al., 2007). 6.4 Integration with other omics approaches Integrating transcriptomic data with other 'omics' approaches, such as proteomics and metabolomics, is
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