Prediction of off-target activities for the end-to-end design of CRISPR guide RNAs

Abstract

The CRISPR-Cas9 system provides unprecedented genome editing capabilities. However, off-target effects lead to sub-optimal usage and additionally are a bottleneck in the development of therapeutic uses. Herein, we introduce the first machine learning-based approach to off-target prediction, yielding a state-of-the-art model for CRISPR-Cas9 that outperforms all other guide design services. Our approach, Elevation, consists of two interdependent machine learning models—one for scoring individual guide-target pairs, and another which aggregates these guide-target scores into a single, overall summary guide score. Through systematic investigation, we demonstrate that Elevation performs substantially better than competing approaches on both tasks. Additionally, we are the first to systematically evaluate approaches on the guide summary score problem; we show that the most widely-used method performs no better than random at times, whereas Elevation consistently outperformed it, sometimes by an order of magnitude. We also introduce an evaluation method that balances errors between active and inactive guides, thereby encapsulating a range of practical use cases; Elevation is consistently superior to other methods across the entire range. Finally, because of the large scale and computational demands of off-target prediction, we have developed a cloud-based service for quick retrieval. This service provides end-to-end guide design by also incorporating our previously reported on-target model, Azimuth. (https://crispr.ml:please treat this web site as confidential until publication).

CROP: A CRISPR/Cas9 guide selection program based on mapping guide variants

“…CROP also included on-target scores for each guide, including Doench score 10 as well as 30nucleotide sequence bearing N 4 -N 20 -NGG-N 3 (N 20 = 20nt guide sequence) format for calculating Azimuth score through Machine learning-based end-to-end CRISPR/Cas9 guide design (https://crispr.ml/) website 11,20 . In addition to scoring, the program also aligned all the mapped variants to the original guide sequence and marked mismatches, highlighting the mismatches for all pairs.…”

Optimized sgRNA design by deep learning to balance the off-target effects and on-target activity of CRISPR/Cas9

“…Several computational methods already exist to predict off-target sites and/or evaluate the specificity of the sgRNAs (18,28,33,(41)(42)(43)(44)(45)(46)(47)(48)(49)(50). Two main features are used to predict the specificity of the sgRNA: number and loci of mismatches, binding energy between sgRNA and target DNA.…”

“…These tools are designed to assist researchers in the selection of best target sites by helping them exclude undesirable targets based on predicted low efficiency or a high potential for off-target effects. They could be broadly divided into two groups, on-target cleavage efficiency tools (6,(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39) and off-target activity tools (18,28,33,(40)(41)(42)(43)(44)(45)(46)(47)(48)(49)(50)(51). In the on-target cleavage efficiency evaluation tools, researchers focus on identifying the gRNA sequence features that contribute to target cleavage efficiency.…”

Advances and challenges in genetic engineering of microalgae

“…The best way to mitigate off-target effects is to know when and where they occur, and then design guides to avoid them while balancing for on-target efficiency. In order to evaluate the off-target incorporation of DNA, numerous techniques have been developed, such as genomewide unbiased identification of double-strand breaks enabled by sequencing (GUIDE-seq), high-throughput genomewide translocation sequencing (HTGTS), integrase-defective lentiviral vector (IDLV) capture, Digenome-seq (Cas9 nucleasedigested whole genome), CIRCLE-seq (comprehensive in vitro reporting of cleavage effects by sequencing), selective enrichment and identification of adapter-tagged DNA ends by sequencing (SITE-seq), BLESS/BLISS (direct in situ breaks labelling, enrichment on streptavidin and next-generation sequencing/breaks labelling in situ and sequencing) (Listgarten et al 2018).…”