RNAS-sgRNA: Recurrent Neural Architecture Search for Detection of On-Target Effects in Single Guide RNA

Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/Cas9 is a leading genomic editing tool, but its effectiveness is limited by considerable heterogeneity in target efficiency among different single guide RNAs (sgRNA). This study presents RNAS-sgRNA, a hybrid model that integrates neural architecture search (NAS) with recurrent neural networks (RNN) to evaluate the on-target efficacy of CRISPR/Cas9 sgRNA. The RNAS-sgRNA model automates architectural discovery, improving sgRNA sequence categorization without considerable manual adjustment. The NAS component improves the RNN architecture, which analyzes sgRNA sequences represented as binary matrices and produces a classification score. Upon evaluation across several datasets, RNAS-sgRNA exhibits substantial performance enhancements with multiple cell lines, comparing its area under the receiver operating characteristic curve (AUROC) performance to the baseline CRISPRpred(SEQ) and DeepCRISPR models. RNAS-sgRNA demonstrated substantial improvements in AUROC performance in several cell lines compared with existing models. Notable improvements include enhancements of 8.62% for HCT116, 121.57% for HEK293T, 13.40% for HeLa, and 20.78% for HL60 cell lines, resulting in an overall improvement of 13.46%. Compared with DeepCRISPR, the model achieved additional AUROC gains in all cell lines tested, with an average improvement of 14.74%. The study also highlighted the ability of the model to deliver superior performance on smaller datasets through transfer learning, underscoring its potential applications in personalized medicine and genetic research. RNAS-sgRNA introduces a novel integration of NAS with RNN to evaluate the efficacy of CRISPR/Cas9 sgRNA. Unlike traditional methods that require significant manual adjustments, this model automates architectural discovery, optimizing the RNN structure for sgRNA sequence analysis. Furthermore, the application of transfer learning to fine-tune the pretrained model on small cell-line datasets represents a pioneering approach in the domain. The model's demonstrated ability to significantly outperform existing algorithms, including CRISPRpred(SEQ) and DeepCRISPR, across multiple cell lines highlights its innovative contribution to genomic editing research and personalized medicine.