AI-Driven Bioinformatics for Precision Genome Editing Simulations

Abstract

Precision genome editing has revolutionized biological research and therapeutic interventions by enabling targeted modifications at the nucleotide level. However, the complexity of genomic contexts and the multifaceted interactions governing editing outcomes demand sophisticated computational approaches to predict and optimize editing efficiencies and specificities. This manuscript introduces an AI-driven bioinformatics framework tailored for precision genome editing simulations. Leveraging deep learning architectures—convolutional neural networks (CNNs), recurrent neural networks (RNNs), and attention-based transformers—our framework integrates sequence features, chromatin accessibility data, and repair pathway biases to forecast editing outcomes across diverse genomic loci. We develop a simulation engine that models Cas9 and base editor activities under variable cellular conditions, validated against empirical datasets from CRISPR–Cas9 and prime editing assays. A comprehensive statistical analysis, presented in a tabular format, quantifies the relative contributions of feature categories to predictive performance. Simulation studies demonstrate that our AI-driven models achieve high accuracy (mean F1-score > 0.85) in predicting insertion–deletion (indel) profiles and base conversion rates, outperforming conventional rule-based predictors by over 20%. The results underscore the potential of machine learning–based simulations to streamline guide RNA design, reduce off-target risks, and accelerate experimental planning. We conclude by discussing limitations and proposing future extensions to incorporate epigenetic modifications and multi-omics data for next-generation genome editing informatics.