期刊
FRONTIERS IN PHARMACOLOGY
卷 9, 期 -, 页码 -出版社
FRONTIERS MEDIA SA
DOI: 10.3389/fphar.2018.00749
关键词
CRISPR-Cas9; bioinformatics; off-target finder; activity prediction; chromatin; machine learning
资金
- Commonwealth Scientific and Industrial Research Organisation
- CSIRO Synthetic Biology Future Science Platform
Recent years have seen the development of computational tools to assist researchers in performing CRISPR-Cas9 experiment optimally. More specifically, these tools aim to maximize on-target activity (guide efficiency) while also minimizing potential off-target effects (guide specificity) by analyzing the features of the target site. Nonetheless, currently available tools cannot robustly predict experimental success as prediction accuracy depends on the approximations of the underlying model and how closely the experimental setup matches the data the model was trained on. Here, we present an overview of the available computational tools, their current limitations and future considerations. We discuss new trends around personalized health by taking genomic variants into account when predicting target sites as well as discussing other governing factors that can improve prediction accuracy.
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