Computational Molecular Biology 2025, Vol.15, No.5, 245-253 http://bioscipublisher.com/index.php/cmb 25 0 these highly similar sequences, and if not careful, they may target similar family members as "targets", leading to some unwanted editing changes. This not only may affect the final phenotype, but also make subsequent analysis more troublesome. For this reason, models that are closer to the structure of the tomato genome are more urgent. They need to be able to handle this complexity more accurately and make the editing results more controllable. 6.2 Limited algorithm adaptability to different CRISPR systems (e.g., Cas12a vs Cas9) At present, most design tools seem to be more inclined towards Cas9, while systems like Cas12a are often not fully considered due to different PAM requirements and cutting methods (Tiwari et al., 2023). This leads to a practical problem: some tomato targets are actually more suitable for Cas12a, but the algorithm cannot provide ideal design suggestions. This "incompatibility" among systems also limits researchers from making full use of various CRISPR variants. To truly expand the scope of editing and ensure that different tools perform their respective duties, the adaptability of algorithms will eventually have to be improved. 6.3 Need for integration with transcriptome and epigenomic datasets to enhance target selection Most of the existing CRISPR design algorithms are based on the DNA sequence itself, which is of course useful. However, in the tomato crop, factors such as gene expression and chromatin openness can also affect whether the editing is smooth (Cardi et al., 2023). Some regions, although the sequence looks appropriate, have unsatisfactory editing effects due to tight chromatin or special expression features. Therefore, to select more functional targets, it would be more reliable to incorporate transcriptome and epigenomic data into the design process. After incorporating this information, the selection of gRNA can also be more in line with the actual breeding and research needs. 7 Future Directions in CRISPR Design Tool Development 7.1 Incorporation of machine learning and AI to improve guide RNA design In the genome editing design of tomatoes, future tools are likely to increasingly rely on machine learning and artificial intelligence. The main reason is not the technological trend, but that rule-based algorithms in the past often appeared inaccurate when facing complex sequences (Naeem et al., 2024). AI methods can learn from a large amount of data from edited experiments which grnas perform better and which are more likely to go off-target, and provide more detailed predictions based on this. Sometimes, it can even automatically adjust the design parameters based on the genomic characteristics of tomatoes themselves to avoid unnecessary mutations. From an overall trend perspective, the design of gRNA is likely to become more dynamic rather than relying on fixed scoring rules. 7.2 Development of tomato-specific databases and genome editing platforms Although many general-purpose CRISPR tools are already available, there is still a lack of truly "tailor-made" databases and platforms for tomato research. If genomic, transcriptomic and even phenotypic information could be integrated into a unified system, target selection would be more based and it would be easier to design multiple editing strategies (Vu et al., 2020). Some tools like CRISPR-GuideMap have demonstrated the possibility of this direction. They can track large-scale sgRNA libraries and are quite helpful in addressing the issues of gene redundancy and functional overlap in tomatoes. If these platforms can be more systematic in the future, tomato breeding and gene function research will proceed more smoothly. 7.3 Real-time, high-throughput screening integration for rapid validation of gRNAs In the verification of gRNA, if it is completely dependent on step-by-step experiments, the efficiency is often not high enough. Combining real-time high-throughput screening with CRISPR design tools can detect a large number of candidate sequences at one time, saving a lot of time (Hu et al., 2019). For instance, through the barcode gRNA tracking system, the performance of different editing sites can be evaluated synchronously without having to test each one individually. In this way, the cycle of design, verification, and re-optimization will be completed more quickly, and grnas with stable performance and fewer off-targets can be found earlier. For scientific research and breeding, this way of enhancing speed and accuracy is undoubtedly more practical.
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