CRISPR–Cas9 gRNA efficiency prediction: an overview of predictive tools and the role of deep learning
Citations Over TimeTop 1% of 2022 papers
Abstract
The clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPR-associated protein 9 (Cas9) system has become a successful and promising technology for gene-editing. To facilitate its effective application, various computational tools have been developed. These tools can assist researchers in the guide RNA (gRNA) design process by predicting cleavage efficiency and specificity and excluding undesirable targets. However, while many tools are available, assessment of their application scenarios and performance benchmarks are limited. Moreover, new deep learning tools have been explored lately for gRNA efficiency prediction, but have not been systematically evaluated. Here, we discuss the approaches that pertain to the on-target activity problem, focusing mainly on the features and computational methods they utilize. Furthermore, we evaluate these tools on independent datasets and give some suggestions for their usage. We conclude with some challenges and perspectives about future directions for CRISPR-Cas9 guide design.
Related Papers
- → Efficient CRISPR/Cas9 genome editing with low off-target effects in zebrafish(2013)492 cited
- → CRISPR-Cas9 system: A new-fangled dawn in gene editing(2019)310 cited
- → Temperature effect on CRISPR-Cas9 mediated genome editing(2017)108 cited
- → Therapeutic applications of CRISPR RNA-guided genome editing(2016)25 cited
- → CRISPR/Cas9 system: A promising technology for the treatment of inherited and neoplastic hematological diseases(2018)17 cited