P0821
WheatCRISPR: A Web-Based Optimized sgRNA Designer for CRISPR-Cas9-Mediated Genome Editing in Wheat

Dustin Cram , National Research Council Canada, Saskatoon, SK, Canada
Manoj Kulkarni , National Research Council, Saskatoon, Saskatoon, SK, Canada
Kevin Rozwadowski , Agriculture and Agri-Food Canada, Saskatoon, SK, Canada
Andrew G. Sharpe , Global Institute of Food Security, Saskatoon, SK, Canada
Sateesh Kagale , National Research Council Canada, Saskatoon, SK, Canada
CRISPR-Cas9 has become a promising technique for crop improvement as it can facilitate fast and efficient molecular breeding without retention of transgene components in the final plant line. Although CRISPR-Cas9 applications promise to change the pace and course of crop improvement, a number of hurdles exist that limit full exploitation of this innovative technology, especially in crops with large, polyploid genomes. In wheat, due to the large size of gene families and very large genome, off-target single guide RNA (sgRNA) binding and cleavage is one of the most critical issues. Lack of bioinformatics tools to facilitate the design of highly specific functional gRNAs and prediction of off-target sites in wheat is currently a significant obstacle to effective application of CRISPR to wheat improvement. We have designed a web-based bioinformatics tool for automated design of highly specific sgRNAs for CRISPR-Cas9 mediated genome editing and CRISPR-based transcriptional regulation of gene expression in wheat. An effective sgRNA should have both high on-target activity and low off-target activity. A recent paper by Doench et al. (Nature Biotechnology 34: 184-191) has used large-scale empirical evidence to devise two models for predicting the on-target activity and off-target effects, respectively. We apply these prediction models to determine on-target specificity and potential off-target activity for individual sgRNAs designed to target specific loci in the wheat genome. Our tool allows researchers to browse all possible sgRNAs targeting a gene of interest and select them based on their predicted on-target and off-target activity, as well as other characteristics such as position within the targeted gene.