CRISPR/Cas9 has transformed gene editing, enabling precise genetic modifications across species. However, existing sgRNA design prediction models based on in vitro data are difficult to generalize to in vivo contexts. In particular, approaches based on single-sgRNA design require additional filtering of in-frame mutations, which is inefficient in terms of both time and cost. In this study, we developed the first mammalian in vivo-trained prediction model to evaluate the efficiency of a dual-sgRNA-based exon deletion strategy. Using 230 editing outcomes of postnatal viable individuals, eight prediction models were constructed and evaluated based on generalized linear models and Random Forests. The final selected model, a Combined GLM, integrated the DeepSpCas9 score with k-mer sequence features, achieving an AUC of 0.759 (95 % Confidence Interval: 0.697–0.821). Motif analysis revealed that CC sequences were associated with high efficiency and TT sequences were associated with low editing efficiency. This study demonstrates that integrating sequence-based features with existing design scores can improve sgRNA efficiency prediction in vivo. The proposed framework can be applied to the development of next-generation sgRNA design tools, with implications for gene therapy, effective animal model generation, and precision genome engineering.
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