Artificial Intelligence in CRISPR-Cas Systems: A Review of Tool Applications

Abstract

Genetic engineering is a method used to alter an organism's DNA, which could entail altering a base pair, removing a section of DNA, or introducing a new DNA segment. Over time, genetic engineering has progressed from basic cloning for research purposes to advanced synthetic biology, leading to new biomedical applications. Targeted genomic editing is one method of cellular reprogramming that aims to change the state of a cell. The invention of CRISPR Cas systems has greatly simplified gene editing. These systems use a unique RNA-guided DNA endonuclease, a protein that can cut DNA and be trained to target new places by changing the sequence of its guide RNA. Integrating CRISPR-Cas systems with artificial intelligence opens new insights into the study of genetic engineering and its applications. Extensive research utilizing deep learning and machine learning has been conducted to predict the outcomes of CRISPR-Cas9 editing. Artificial intelligence also predicts RNA editing events and CRISPR off-target cleavage sites. Scientists often struggle to identify the ideal perturbation for their specific application because of the ample search space and expensive genetic trials. The algorithmic method using artificial intelligence utilizes the cause-and-effect link between variables in a complicated system like genome regulation to determine which perturbation is most effective in each successive round of testing, thereby making artificial intelligence an effective technique in gene editing.

Keywords: Artificial intelligence; CRISPR-cas; Deep learning; Gene editing; Guide RNA activity; Machine learning.