Computational Molecular Biology 2025, Vol.15, No.5, 245-253 http://bioscipublisher.com/index.php/cmb 24 9 deletion that has occurred in some wild tomatoes (Figure 2) (Berman et al., 2025). Therefore, the main objective of this case is to design a set of grnas that can precisely induce this functional deficiency, enabling tomatoes to acquire lasting resistance without sacrificing breeding efficiency. Figure 2 Schematic overview illustrating the library workflow, from design to screening (Adopted from Berman et al., 2025) 5.2 Step-by-step use of CRISPR-P and CRISPR-GE tools for gRNA design and validation In practical operation, instead of directly starting from one or two candidate sequences, we first generated a large number of possible gRNAs through CRISPR-P and CRISPR-GE. Subsequently, screening was carried out step by step based on conditions such as whether PAM matched, potential off-target situations, MIT-specific scores, distances from natural ol-2 sites, and stability of sequence secondary structures (Prajapati and Nain, 2021). The remaining gRNA needs further verification of cutting efficiency and specificity. Only those with stable performance will enter the transformation stage. With the help of these tools, the process of selecting highly specific grnas has become clearer, and the additional costs caused by off-target during the experimental stage have also been reduced. 5.3 Outcomes: editing efficiency, off-target analysis, and phenotypic evaluation of powdery mildew resistance CRISPR editing on SlMLO1 performed quite well, and biallelic mutations were observed in the first-generation plants (Brooks et al., 2014). Subsequent off-target detection also revealed that non-target sequences were hardly affected, indicating that the algorithm-assisted design indeed improved the accuracy of editing. More direct evidence comes from phenotypic results: these edited strains demonstrated complete resistance in powdery mildew inoculation trials, confirming the inactivation of SlMLO1 and also demonstrating the value of CRISPR design algorithms in disease-resistant breeding. This case demonstrates that the combination of computational tools and genome editing can more efficiently promote the improvement of crop traits. 6 Challenges in Applying Design Algorithms to Tomato 6.1 Inaccuracies in off-target prediction for the complex tomato genome In the tomato genome, there are many repetitive sequences and genes from the same family often look very similar, which often makes off-target prediction less reliable. Existing algorithms often struggle to distinguish
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