# Computational Tools and Resources Supporting CRISPR-Cas Experiments
## 3 Guide RNA Design  
The problem of designing a specific and efficient gRNA may be avoided in certain cases by referring to existing libraries of validated gRNAs. These libraries offer assistance in choosing gRNAs with confirmed activity for specific applications; e.g., CRISPRInc offers validated guides for lncRNAs, whereas CRISPRz is a database of validated CRISPR targets in zebrafish [55; 56]. Additionally, Addgene delivers a list of prevalidated gRNA sequences for various species and applications, as well as genome-wide libraries of sgRNAs for gene knockout experiments [57; 58]. However, these validated gRNAs are usually available only for frequently targeted _loci_ such as GFP and for generic experiments. Additionally, to reproduce their activity, one needs to recreate the experimental condition, which is not necessarily possible or desirable. This limitation gives rise to a demand for custom gRNAs tailored for every particular purpose. The main group of CRISPR-Cas computational tools is, therefore, dedicated to aid in gRNA design. At the moment of writing of this manuscript, the WeReview database indexes more than 60 tools in this category [59]. The programs usually score the possible guides according to the predicted cleavage efficiency (maximizing on-target activity) and specificity (minimizing off-target activity). The predictive models differ in the theory underlying the scoring methods and in generated results, forming a large and diverse category. The observed variation in the activity of gRNAs has led to several attempts at identifying the relationship between the guide sequence characteristics and the efficiency of Cas cleavage. The problem was approached by conducting large-scale screening experiments analyzing the activity of hundreds or thousands of gRNAs at different _loci_ by high-throughput methods [10; 15; 16; 17; 19; 60; 61; 62; 63]. These efforts resulted in sets of rules for the design of highly active gRNAs and predictive models that were subsequently implemented in public design tools (sgRNA Designer, SSC, CRISPRscan, sgRNA Scorer,CRISPRRater) [16; 17; 19; 60; 62]. These models differ in weighting particular features, such as nucleotides preferred at every position, thermodynamics of secondary structures, occurrence of the same nucleotide tracts or GC content. [PERSON] et al. tested a pool of 1841 sgRNAs aiming at all possible targets of a panel of six mouse and three human genes coding cell surface markers and tested quantitatively their ability to generate biallelic knockouts by fluorescence-activated cell sorting (FACS), thereby separating the most active sgRNAs [63]. They identified sequence features that predict sgRNA efficacy: guanine was strongly preferred whereas cytosine was strongly unfavorable at position 20 counting from the 5' end, the opposite was true for the position 16, and thymine was unfavored in the 3' end region of the sgRNA which is related to the termination signal of the Pol III transcription machinery used to express the sgRNAs. Additionally, sgRNAs with low or high GC content appeared to be less active. The authors used the obtained data to determine sequence feature weights for activity predictions and built a predictive model for sgRNA activity by training a logistic regression classifier to identify sgRNAs exhibiting the highest activity for each gene using sequence features. [PERSON] et al. continued their research and proposed improvements to their model [16]. They used screening data generated with human and mouse genome-wide sgRNA libraries designed according to the previously proposed rules. They also included in the model additional features such as position-independent nucleotide counts, the location of the sgRNA target site within the gene, and thermodynamic properties of the sgRNAs. The authors designated the new model as Rule Set 2 and demonstrated its improved performance versus Rule Set 1 across different data sets. The Rule Set 2 was implemented subsequently in many gRNA design tools, such as CHOPCHOP, CRISPR, GPP sgRNA Designer, or Benchling.