Machine learning based CRISPR gRNA design for therapeutic exon skipping.

Restoring gene function by the induced skipping of deleterious exons has been shown to be effective for treating genetic disorders. However, many of the clinically successful therapies for exon skipping are transient oligonucleotide-based treatments that require frequent dosing. CRISPR-Cas9 based genome editing that causes exon skipping is a promising therapeutic modality that may offer permanent alleviation of genetic disease. We show that machine learning can select Cas9 guide RNAs that disrupt splice acceptors and cause the skipping of targeted exons. We experimentally measured the exon skipping frequencies of a diverse genome-integrated library of 791 splice sequences targeted by 1,063 guide RNAs in mouse embryonic stem cells. We found that our method, SkipGuide, is able to identify effective guide RNAs with a precision of 0.68 (50% threshold predicted exon skipping frequency) and 0.93 (70% threshold predicted exon skipping frequency). We anticipate that SkipGuide will be useful for selecting guide RNA candidates for evaluation of CRISPR-Cas9-mediated exon skipping therapy. Author summary: One form of genetic therapy is exon skipping, where a cell is forced to exclude problematic exons from a mutant transcript such that the resultant protein is functional. Recent studies show that CRISPR technology can induce therapeutic exon skipping. By using a specific guide RNA, targeted disruption of an exon's splice acceptor sequence can be performed, which can result in its skipping. However, an exon may have many candidate guide RNAs that target its splice acceptor, and not all guide RNAs will lead to a sufficient level of exon skipping. A predictive method that can identify a guide RNA that will cause an exon to be skipped would be useful for guiding therapeutic development efforts. We present SkipGuide, a machine learning method for predicting the exon skipping level caused by a guide RNA that targets its splice acceptor region. To develop and evaluate SkipGuide, we experimentally measured the skipping levels of a diverse set of exons targeted by multiple guide RNAs in a mouse cell line. We demonstrate that SkipGuide can accurately identify the guide RNAs that lead to high levels of exon skipping. [ABSTRACT FROM AUTHOR]