# Computational Tools and Resources Supporting CRISPR-Cas Experiments
## 4 Repair Outcome Predictions  
The relatively new branch of tools utilizes the fact that DNA repair is, to a certain extent, a deterministic process. The tools allow us to predict Cas edit results by the analysis of microhomologies and by exploiting the biases in the repair outcome. High-fidelity prediction of DSB repair outcomes is one of crucial concerns in CRISPR-Cas experimental design. Depending on the purpose of genome editing different repair products are desired, such as frame shift indels used for gene knockout or, contrarily, in-frame deletions maintaining the transcriptional activity of the gene. Genome editing outcomes are determined by the DNA repair pathway, which is in turn influenced by many factors, including the cell cycle stage and repair-related proteins expressed in a given cell type [78]. The most common DSB repair mechanisms, canonical nonhomologous end joining (c-NHEJ) and alternative nonhomologous end joining (alt-NHEJ), including microhomology mediated end joining (MMEJ),often lead to insertion or deletion at the cutting site [79]. It has been proven that the distribution of repair outcomes is not random but strongly reproducible within a cell population and depends mainly on the targeted sequence. Two dominant categories of mutations occurring in the effect of DSB repair has been revealed: NHEJ-dependent single base insertion (duplication of nucleotide in position -4 from PAM) and MH-mediated deletion. [4; 6; 80; 81] These observations were adopted by the developers of bioinformatic tools dedicated to detailed characterization of repair outcomes and improve experimental design. A comparison of these tools is presented in Table 1[3; 7; 82]. [PERSON] et al. and [PERSON] et al. created online tools for Cas9 edit result prediction (FORECasT and InDelphi, respectively) based on similar experiments [3; 7]. Both groups used artificial constructs containing a gRNA expressed under the human U6 promoter and target sequence complementary to the guide flanked by different nucleotide contexts. Dozens of these constructs were transfected into cell lines stably expressing Cas9, leading to edits of the provided target sites [3; 7]. [PERSON] et al., used an endogenous model to develop an online tool named SPROUT. The authors targeted 1656 genomic _loci_ in primary CD4\({}^{+}\) T cells isolated from 18 human donors and used an RNP complex containing crRNA, tracrRNA and the Cas9 protein [82]. In each experiment, cut-site-proximal repair products were characterized after PCR amplification of target sites with their flanking regions and deep-sequencing, revealing a sequence-dependent mutation profile. Then, data from sequencing were used to train algorithms developed by the authors to precisely predict DSB repair outcomes, and subsequently, the accuracy of prediction was tested. Each algorithm exhibited high accuracy of repair profile prediction for a given cell type [82]. The algorithms developed by authors are available online. SPROUT requires only sequences of the gRNA and PAM (total of 23 nt) to output information regarding predicted results, including the fraction of total reads with insertion, insertion to deletion ratio, average insertion and deletion length and the inserted base pair. SPROUT does not show any information about sequences of outcomes [82]. FORECasT requests the target DNA sequence and a location of PAM to output sequences of the ten most likely results and percentage of in-frame mutations [3]. InDelphi provides many capabilities and information not presented in other tools. Aside from sequences of predicted outcomes with their