A systematic review of computational methods for designing efficient guides for CRISPR DNA base editor systems

In only a few years, as a breakthrough technology, clustered regularly interspaced short palindromic repeats/CRISPR-associated protein (CRISPR/Cas) gene-editing systems have ushered in the era of genome engineering with a plethora of applications. One of the most promising CRISPR tools, so-called base editors, opened an exciting avenue for exploring new therapeutic approaches through controlled mutagenesis. However, the efficiency of a base editor guide varies depending on several biological determinants, such as chromatin accessibility, DNA repair proteins, transcriptional activity, factors related to local sequence context and so on. Thus, the success of genetic perturbation directed by CRISPR/Cas base-editing systems relies on an optimal single guide RNA (sgRNA) design, taking those determinants into account. Although there is 11 commonly used software to design guides specifically for base editors, only three of them investigated and implemented those biological determinants into their models. This review presents the key features, capabilities and limitations of all currently available software with a particular focus on predictive model-based algorithms. Here, we summarize existing software for sgRNA design and provide a base for improving the efficiency of existing available software suites for precise target base editing.