RNA-Targeting CRISPR: AI Precisely Controls Gene Expression

According to a new study, artificial intelligence can forecast the on- and off-target behavior of CRISPR tools that target RNA rather than DNA. To manage the expression of human genes in various ways, the study combines a deep learning model with CRISPR screens—for example, by flipping a switch to fully turn them off or turning a dimmer knob to reduce their activity slightly. New CRISPR-based treatments might be created using these precise gene controls. To comprehend RNA control and determine the role of non-coding RNAs, researchers developed a platform for RNA-targeting CRISPR screening utilizing Cas13. Since RNA serves as the primary genetic component of viruses like SARS-CoV-2 and the flu, RNA-targeting CRISPRs can potentially lead to the development of fresh approaches for preventing or treating viral illnesses. Additionally, the production of RNA from the genome’s DNA occurs as one of the first stages in human cells when a gene is expressed.

Maximizing the activity of RNA-targeting CRISPRs on the desired target RNA and minimizing activity on other RNAs that might have negative side effects for the cell are important objectives of the study. Mismatches between the guide and target RNA and insertion and deletion mutations are examples of off-target action. Prior research on RNA-targeting CRISPRs concentrated solely on on-target activity and mismatches; off-target activity prediction, particularly for insertion and deletion mutations, has received less attention. Insertions or deletions make for around one in five mutations in human populations; as such, these are significant classes of possible off-targets to take into account for CRISPR design.

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