Rice Genomics and Genetics 2024, Vol.15, No.4, 164-177 http://cropscipublisher.com/index.php/rgg 167 in nutrient uptake and stress tolerance. Additionally, the use of proteogenomics has revealed the complexity of the rice transcriptome, identifying numerous fusion and intergenic transcripts, as well as a vast array of splicing isoforms, which contribute to the functional diversity of gene families (Figure 2) (Chen et al., 2020; Zanini et al., 2021). The functional impact map of genetic variants (GVs) in rice further elucidates the roles of missense mutations and chromatin accessibility in gene regulation, providing a valuable resource for gene cloning and functional studies (Zhou et al., 2018). Figure 2 Comparative analysis of fusion transcripts (Adopted from Chen et al., 2020) Image caption: A: Venn diagram showing the overlapping and unique fusion transcripts identified by srRNA_seq and lrRNA_seq; B: Summary of fusion transcript subtypes; C: Circos representations of fusion transcripts consisting of two genes; D: Venn diagram presenting the overlapping and unique genes involved in fusion transcript formation; E: GO and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of fusion genes; F: Loci frequency present in fusion transcripts; G: RT-qPCR validation of fusion transcripts; Red arrows indicate the validated fusion transcripts. F1 to F3: Three fusion transcripts; M: marker (Adopted from Chen et al., 2020) Several tools and databases have been developed to facilitate the functional annotation of rice genes. The eggNOG-mapper v2, for example, offers comprehensive functional annotation, orthology assignments, and domain prediction, optimized for large-scale genomic data sets (Cantalapiedra et al., 2021). The IC4R-2.0 annotation system integrates multiple omics data, including long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs), to enhance the accuracy and completeness of rice genome annotations. These resources are crucial for comparative and functional genomic studies, providing a robust framework for understanding gene functions in rice.
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