CRISPRscan is a novel algorithm to predict gRNA efficiency.
Based on a large scale analysis of sgRNA mutagenesis activity in zebrafish, we established rules to predict sgRNA activity in vivo and build the CRISPRscan model integrating these rules. We independently validated with success our predictions using sgRNAs different from the large scale analysis.
CRISPRscan searches for potential genomic off-targets with the following rules.
According to Hsu et al.Nature Biotechnology 2013, potential off-targets can have a maximum of 2 mismatches with the sgRNA.
With the method published by Cong et al.Science 2013, potential off-targets must match perfectly in their seed (12 nt 3' of the PAM sequence) and a maximum of 2 mismatches in the rest of the sgRNA. This rule is more stringent than the All method and therefore less off-targets are found.
CFD (Cutting Frequency Determination)
Doench et al.Nature Biotechnology 2016 measured the cutting efficiency of potential off-targets and integrated them into the CFD score. Potential off-targets with up to 4 mismatches are scored with Doench et al. matrix.
Potential off-targets can have a maximum of 2 mismatches with the gRNA.
The experiments published by Kim et al.Nature Methods 2017 support potential off-targets that must match perfectly in their seed (6 nt 3' of the PAM sequence) and a maximum of 2 mismatches in the rest of the gRNA. This rule is more stringent than the All method and therefore less off-targets are found.
Detailed information can be found in our protocol.