A groundbreaking study published in Nature Biotechnology reveals that artificial intelligence can now predict the on- and off-target activity of CRISPR tools that target RNA rather than DNA. This research, conducted by scientists from New York University, Columbia University, and the New York Genome Center, combines deep learning with CRISPR screens to control the expression of human genes in various ways. The precision of these gene controls holds promise for the development of new CRISPR-based therapies.
CRISPR, a widely used gene editing technology, typically targets DNA using the enzyme Cas9. However, recent discoveries have identified a different type of CRISPR that targets RNA using the enzyme Cas13. RNA-targeting CRISPR tools have a wide range of applications, including RNA editing, blocking gene expression, and screening for potential drug candidates. They also show promise in preventing or treating viral infections, as RNA is the main genetic material in viruses like SARS-CoV-2.
While previous studies focused on the on-target activity and mismatches of RNA-targeting CRISPRs, this study delves into predicting off-target activity, including insertion and deletion mutations. With approximately one in five mutations in human populations being insertions or deletions, understanding and predicting off-target effects are crucial for the design and development of effective CRISPR therapies.
The research team utilized a deep learning model called TIGER (Targeted Inhibition of Gene Expression via guide RNA design) to analyze data from CRISPR screens and accurately predict both on-target and off-target activity of RNA-targeting CRISPR tools. The predictions generated by TIGER outperformed previous models, providing a valuable tool for the field of RNA-targeting CRISPR research and therapeutics.
This breakthrough study not only enhances our understanding of RNA-targeting CRISPRs but also opens up new possibilities for precise gene modulation and the development of innovative RNA-targeting therapies. By combining artificial intelligence and the power of CRISPR technology, this research paves the way for future advancements in the field of biomedicine.
More information:
Prediction of on-target and off-target activity of CRISPR–Cas13d guide RNAs using deep learning, Nature Biotechnology (2023). DOI: 10.1038/s41587-023-01830-8
Citation:
AI combined with CRISPR precisely controls gene expression (2023, July 3)
retrieved 3 July 2023
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