# Computational Tools and Resources Supporting CRISPR-Cas Experiments
## 3 Guide RNA Design
A different approach was proposed by [PERSON] et al. [19]. They analyzed the molecular features that influence sgRNA activity and stability in vivo by using 1280 sgRNAs targeting 128 genes in the zebrafish genome. They injected in vitro transcribed sgRNAs together with Cas9-encoding mRNA into zebrafish embryos at the one-cell stage. The use of in vitro transcribed sgRNAs allowed the authors to focus on the influence of sgRNA stability and its relation to the sequence composition of the sgRNA. They demonstrated that more stable sgRNAs exhibit guanine enrichment and adenine depletion and, predictably, that the sgRNA stability positively correlates with the efficiency of the system. The authors identified also sequence features of the most active sgRNAs. The region spanning nucleotides 1-14 exhibited guanine enrichment, and positions 15-18 were characterized by cytosine enrichment. Thymidines and adenines were generally unflavoured, with the exception of positions 9 and 10. The authors applied randomized logistic regression to select the main features that determine the sgRNA efficiency. Then, they trained a linear regression model to predict the activity of a given sgRNA and implement it into the CRISPRscan tool [19]. It has been demonstrated, however, that the predictive accuracy of the many developed models is variable, and there is no superior algorithm in terms of reliability across different datasets [64; 65]. The most plausible explanation for the observed inconsistency is that different experimental settings are used to generate the training datasets for every predictive model. A particular distinction identified to be of importance is the method of gRNA expression and delivery. As described earlier, the delivery modality (plasmid and viral vs. mRNA or RNP complex) can impact the cleavage kinetics and, consequently, the efficiency of edits. The majority of experiments were conducted in cell cultures and utilized the U6 promoter for relatively long-term and strong gRNA expression from a plasmid, whereas the other group of studies used the T7 promoter for in vitro transcription, and then, the gRNAs were injected into zebrafish embryos resulting in a short editing window. Thus, the general advice is to use a predictive algorithm that is based on an expression system analogous to the planned experiment. Specifically, [PERSON] et al. suggested using the Doench score to rank the activity of gRNAs expressed from plasmids and the Moreno-Mateos score for gRNAs transcribed in vitro [64]. Another source of inconsistency among predictive algorithms is based on different approaches to gRNA activity assessment. A subset of studies used an indirect measurement and trained their models on data based on phenotypic changes, e.g., protein expression or drug resistance [16; 63]. These models may be more appropriate in cases of functional studies but require reproduction of the original experimental conditions to achieve high accuracy. Another group of studies measured Cas9 activity directly by sequencing the targeted sites and by indel detection [19; 60]. The applicability of the resulting models is therefore wider. Some tools integrate several models in their predictions (CRISPOR, CHOPCHOOP, E-CRISP), allowing the user to evaluate different scores [64; 66; 67; 68]. The problem of Cas9 off-target activity prediction was initially approached by using common sequence alignment algorithms such as BWA or Bowtie [68]. This approach offers, however, moderate usefulness due to the problem of falsely positive and falsely negative results. Algorithms miss targets with a high number of mismatches, leading to an omission of a proportion of potential off-target sites. On the other hand, they still deliver false positives, as many identified off-target sites are not actually cleaved. [PERSON] et al., demonstrated a considerable discrepancy between results predicted by the MIT CRISPR Design Tool (currently unavailable), E-CRISP, and the off-target sites empirically verified by GUIDE-seq [12].