Rice Genomics and Genetics 2025, Vol.16, No.2, 106-115 http://cropscipublisher.com/index.php/rgg 112 5.2 Networks, pathways and databases: rice research also talks about "relationship diagrams" In rice functional genomics, analyzing a single gene often does not show the whole picture. Genes cooperate and regulate each other to form complex life activities. As a result, the focus of research has gradually shifted to the analysis of networks and pathways. For example, OryzaExpress is a typical example. It integrates gene expression data and omics annotations to form expression networks (GENs). Researchers can use it to see which genes behave similarly in expression and then infer whether they are involved in the same biological process (Hamada et al., 2010). Another example is the CARMO platform, which not only integrates multiple types of data such as transcriptomes, SNPs, and epigenetic modifications, but also groups them into gene modules to facilitate comparisons between different experiments or studies. This approach allows us to extract more general rules from "point" information (Wang et al., 2015). MBKbase goes a step further and not only integrates genomic data, but also takes phenotypic information into account. It provides a number of practical tools, such as directly searching for specific genotypes or visualizing the relationship between genotypes and phenotypes, which is particularly suitable for molecular breeding and functional research (Peng et al., 2019). 6 Outlook and Challenges: The Future of Rice Functional Genomics 6.1 Emerging technologies In recent years, new technologies such as single-cell sequencing and synthetic biology have begun to appear frequently in rice research. They are potential not only because they are "new", but also because they can solve some problems that are difficult to handle with old methods. For example, single-cell sequencing can clearly see the differences between different cells, instead of just looking at the overall average level. This can provide more detailed clues for studying gene expression in rice development and adversity response (Lu et al., 2010). Of course, this technology itself also has problems such as complex data and high cost, and it may not be widely popularized in a short period of time. On the other hand, synthetic biology gives scientists greater freedom to combine or regulate genes like building blocks, and in theory, it can design higher-yield, disease-resistant, and stress-resistant rice varieties (Li et al., 2018). If combined with a high-throughput phenotyping platform, the speed of functional annotation can be even faster. And these advances, in the final analysis, are paving the way for the realization of "green super rice" (Yang et al., 2013). 6.2 Challenges and opportunities in translational functional genomics The current problem is not that we don’t know which genes may be important, but that we know too much and cannot verify them all. The number of genes identified by high-throughput sequencing and various mutagenesis methods is huge, but it is only the tip of the iceberg to really figure out what they do in rice. Although many mutant resource libraries have been established, such as T-DNA insertion libraries, the functional verification of these materials is still time-consuming and laborious (Jiang et al., 2012; Lo et al., 2016). In addition, traditional phenotypic observation relies on manual judgment, which is inevitably subjective and slow. If we really want to transform genetic research into actual results in breeding, we must have supporting automated phenotyping technology (Yang et al., 2013). However, from another perspective, it is these limitations that have promoted cross-disciplinary collaboration. Integrating genomics, phenomics and informatics may be the key to improving efficiency, especially when dealing with complex crops such as rice (Delseny et al., 2001; Guo et al., 2014). 6.3 Precision breeding and the potential of sustainable agriculture Precision breeding sounds like a technical issue, but it is more of a choice issue. Researchers can already find "good" genes related to yield, resistance, and nutrient absorption efficiency at the genomic level (Jiang et al., 2012; Li et al., 2018). Using this information to guide breeding can not only avoid detours, but also greatly shorten the research and development cycle of new rice varieties. However, it should be noted that the implementation of precision breeding is not achieved overnight, it requires long-term technical accumulation and data support. On the other hand, sustainable agriculture is no longer a slogan, especially in the context of climate change and
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