CNN–GRU Hybrid Network for Predicting sgRNA On-Target Activity in Bacteria

The precise prediction of CRISPR–Cas9 single-guide RNA (sgRNA) on-target activity is important for designing an efficient gene editing experiment. Even though there is a large amount of deep learning models that have been raised and proved. When these models are transferred to other species like bacteria, the performance will decrease significantly. There is fundamental difference between genome background and DNA repair/selection. Based on multiple Cas9 variants, we propose CNNGRUHybrid, a hybrid neural architecture that (i) extracts local motif features using multi-scale 1D convolutions, (ii) models longer-range dependencies using stacked bidirectional GRUs (BiGRUs), and (iii) aggregates position-wise representations via a lightweight attention-style pooling mechanism. This model uses one-hot encoded 43-nt or 28-nt windows as input, and incorporates numeric or tabular features optionally like GC content and assay-derived descriptors. We further define and compare our pretraining–finetuning regimes across datasets/variants. The results of our experiment comparing CNNGRUHybrid with representative CNN based models show consistent improvements in rank-based metrics (e.g., Spearman correlation) on bacterial sgRNA activity prediction tasks.