A systematic evaluation of data processing and problem formulation of CRISPR off-target site prediction

Abstract

CRISPR/Cas9 system is widely used in a broad range of gene-editing applications. While this gene-editing technique is quite accurate in the target region, there may be many unplanned off-target edited sites. Consequently, a plethora of computational methods have been developed to predict off-target cleavage sites given a guide RNA and a reference genome. However, these methods are based on small-scale datasets (only tens to hundreds of off-target sites) produced by experimental techniques to detect off-target sites with a low signal-to-noise ratio. Recently, CHANGE-seq, a new in vitro experimental technique to detect off-target sites, was used to produce a dataset of unprecedented scale and quality (more than 200,000 off-target sites over 110 guide RNAs). In addition, the same study included GUIDE-seq experiments for 58 of the guide RNAs to produce in vivo measurements of off-target sites. Here, we fill the gap in previous computational methods by utilizing these data to perform a systematic evaluation of data processing and formulation of the CRISPR off-target site prediction problem. Our evaluations show that data transformation as a pre-processing phase is critical prior to model training. Moreover, we demonstrate the improvement gained by adding potential inactive off-target sites to the training datasets. Furthermore, our results point to the importance of adding the number of mismatches between the guide RNA and the off-target site as a feature. Finally, we present predictive off-target in vivo models based on transfer learning from in vitro. Our conclusions will be instrumental to any future development of an off-target predictor based on high-throughput datasets.

Generating, modeling, and evaluating a large-scale set of CRISPR/Cas9 off-target sites with bulges

“…An essential step in developing an effective offtarget predictor is constructing datasets that include active and inactive OTS (i.e., sites obtained experimentally and potential sites based on sequence similarity that were not cleaved) (32). To obtain all potential OTS, we employed SWOffinder, a novel method we developed for searching CRISPR OTS with bulges (33). Compared to previous search methods such as Cas-OFFinder (34), which were utilized in CRISPR-Net and CRISPR-IP (26; 29), SWOffinder guarantees the detection of all potential OTS of Cas9 RNA-guided endonucleases, both with and without bulges, given a specific sgRNA and a reference genome.…”