CRISPR/Cas is an adaptive immune system of archaea and bacteria, providing a defense against invading plasmids and viruses. The native CRISPR/Cas defense system consists of three phases: adaptation or acquisition, expression or biogenesis, and interference. Reducing the number of potential off-target sites to improve CRISPR/Cas specificity is critical, particularly in human therapeutic applications, germline modifications and genome editing for important agricultural purposes.
CRISPR gene editing may bind to unintended genomic loci for cleavage, known as off-target effects. Off-targeting is a major challenge for the CRISPR/Cas community because Cas9 can bind and create DSBs even if there is only partial complementarity between the sgRNA and the target site. Many studies report that CRISPR/Cas may produce a large number of off-targets. For example, one study in humans found that Cas9 can tolerate up to five mismatches between sgRNA and target sites, resulting in DNA cutting even more frequently than expected at the target site. Off-targeting is not a random variation, but is caused by PAM and target sites.
From a genome editing perspective, CRSIPR off-targeting may lead to undesirable changes at random sites in the genome, thereby compromising the benefits of genome modification. Early prediction and minimization of off-targets is essential for the safe use of CRISPR/Cas, particularly in therapeutic applications and translational research. It is also important to identify all off-targets and confirm that the desired phenotype arises from the target modification rather than from the off-target.
The Cas9: sgRNA ribonucleoprotein complex targets the genomic DNA when present in the cell nucleus. (Vicente M M et al., 2021)
One of the key factors in the off-target effect of CRISPR is the guide RNA (gRNA), which is a combination of RNA sequences that guide the Cas9 nuclease to cut at specific sites on the genome. When designing gRNAs, we can use specific algorithms to predict potential off-target sites and select gRNAs with as low an off-target effect as possible. gRNAs are then sequenced after genome cleavage to verify mutations at these sites to assess the off-target effect. Prediction models and algorithms are essential to select the best efficient gRNAs with limited off-target effects.
A pairing-based approach to sgRNA sequence evaluation involves matching sgRNAs to a reference genome and identifying potential off-targets based on sequence homology.
Hypothesis-driven approaches to predict specificity
Alignment-based methods can reliably predict off-target. However, not all mismatched nucleotide positions in sgRNAs are equally valid for off-target cleavage. These issues can be addressed using the MIT Specificity Score (named after the institution) assessment off-target tool. Cut frequency determination (CFD) scores are also commonly used to assess off-target in CRISPR/Cas.
Learning methods use a variety of features (including PAM, GC content, methylation status and chromatographic structure) to improve their off-target predictions compared to empirical algorithms. Recently there have been a number of tools with multiple features for machine learning to predict CRISPR/Cas system specificity and off-target.
The rapid rise of CRISPR/Cas applications has led researchers to design bioinformatics tools that use different algorithms and design rules to achieve efficient sgRNA design, specific targeting modifications and low off-target rates. Such tools facilitate gRNA design through user-defined PAM sequences and Cas nucleic acid endonucleases to achieve maximum targeting efficiency in the available genome. Many design tools exist, but each has its own advantages and limitations. Most differ in design parameters, specifications, available genomes, on-target efficiency scores, off-target predictions, etc.
More than 200 genomes are available on the CHOPCHOP website; users can input gene name or target sequence. This tool supports gRNA design for multiple applications (KO, KI, CRISPRi, and CRISPRa); users can choose application-specific Cas effector endonuclease. CHOPCHOP ranks potential sgRNAs on position, GC contents, mismatch number, and efficiency scores.
Base Editing (BE)-Analyzer and BE-Designer
These are publicly available design tools for base editing. Both tools help researchers select sgRNA for desired region and analyze outcomes of base editing from NGS data. BE-Designer also lists all potential sgRNAs for a given DNA sequence and provides off-targets for a given sgRNA against a large number of species.
One of the best tools for designing efficient sgRNA, CRISPOR contains 19 different PAMs and 417 different genomes. It can accept genome coordinates or user-provided sequences. Each sgRNA will be ranked for off-targets, specificity, and efficiency. Outcome predictions, GC contents, and poly T will also be given for each sgRNA.
Like CHOPCHOP, CRISFlash can use sequenced genome or genome sequence to design sgRNA. In addition, it accepts user-defined values to optimize sgRNA and off-targets. CRISFlash is considered a faster tool for sgRNA design and scoring off-targets.
E-CRISP is a user-friendly web tool for designing sgRNA with high specificity and efficiency. It provides an option to search for potential sgRNAs within a given genomic region, and it ranks the sgRNAs based on specificity and efficiency. E-CRISP also allows users to customize the number and position of mismatches for off-targets, and it provides detailed information on predicted off-targets.
Cas-Designer is a web-based tool for designing sgRNA for the CRISPR/Cas system. It allows users to select specific Cas enzymes, input target sequences, and evaluate off-target sites. Cas-Designer also provides a visualization tool to help users assess the location and distribution of potential off-target sites.
ZiFiT Targeter is a web-based tool that provides a user-friendly interface for designing sgRNA for the CRISPR/Cas system. It allows users to input target sequences and select Cas enzymes, and it provides a ranking of potential sgRNAs based on specificity and efficiency. ZiFiT Targeter also provides detailed information on potential off-target sites, including the number of mismatches and the location of the off-target site within the genome.