# Computational Tools and Resources Supporting CRISPR-Cas Experiments
## 6 Discussion  
Computational tools serve as valuable support for genome editing experiments. The significant progress in the development of scoring algorithms notwithstanding, the majority of predictive models relay on relatively basic sequence features. There is still room for improvement in terms of the understanding of the exact role of chromatin structure, nucleic acid thermodynamics or the occurrence of bulges in the process of Cas9 cleavage. As mentioned earlier, the issue of chromatin state and DNA accessibility is considered one of the key factors to be analyzed in the context of either the efficiency or specificity of CRISPR-Cas9 cleavage. Indeed, the association between Cas9 cleavage efficiency and chromatin accessibility has been demonstrated [103; 104; 105; 106; 107; 108; 109]. Avoidance of, e.g., strongly positioned nucleosomes may be beneficial in terms of efficacious edits and reliable assessment of the off-target risk. Another important problem is naturally occurring genetic variation. The majority of gRNA design tools refer to the human reference genome and do not include variant information. Thus, SNPs and mutations in target sequences may have considerable effects on cleavage specificity either by disrupting the targeted sequence or by creating unexpected off-target sites [110]. These effects may, in turn, lead to unintended edits of essential genes, e.g., tumor suppressors. In fact, it has been demonstrated that genetic variation leads to a wide distribution of sites containing unexpected PAMs and absent PAMs across the genome [111; 102; 112]. Certain tools incorporate variation information into the process of gRNA design (CRISPROR, WGE, E-CRISP, CCTop). Moreover, there are tools dedicated to variant-specific CRISPR-Cas9 edits (SNP-CRISPR, AlleleAnalyer) [113; 114]. Generally, we suggest using tools that offer more than one scoring algorithm to assess the activity of gRNAs as accurately as possible. By this criterion CRISPR, is the most advisable tool. Additionally, as we described earlier, the variety of additional features makes this tool particularly useful. Nevertheless, the activity and specificity of any custom gRNA need to be empirically validated due to the reported inconsistency of the models, as well as the risk of unexpected genetic variation or heterogeneity of cell populations. Another point to bear in mind is the fact that some models are not universally applicable. For example, the Moreno-Mateos score is best suited for experiments with in vitro expressed gRNAs. At the moment of the manuscript writing, InDelphi is the most advanced predictive tool, providing the deepest analysis of results and several utilities that may be helpful in outcomes analysis. However, the data that the predictive algorithms are built on are limited to only certain cell lines, offering much less reliable estimation for cells with different genetic backgrounds. Cell line-specific DNA repair machinery disruption is a phenomenon that may lead to differential patterns of results obtained among different cell lines. Another issue worth consideration is the fact that accuracy of the predictive tools is moderate in the case of \"difficult\" sequences, such as long tracts of repeated sequences. Of the tools aimed at post experiment results analysis, TIDE is the most straightforward and approachable solution; however, it has important limitations. Its accuracy is reduced in the case of low-frequency outcomes, and because it is a PCR-based method, it may not be well suited for the analysis of sequences containing tracts of tandem repeats. Additionally, the maximum size of indels that this tool detects is limited to 50 base pairs, making it not applicable in the analysis of larger rearrangements. The outlined limitations may be overcome by applying tools based on NGS data analysis such as CRISPR-GA or CRISPReso. The downsides of these approaches are, however, tied to the inherent limitations of NGS techniques. NGS is a costly and time-consuming system and, in many cases, requires the use of an external service.