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transEDIT-dual gRNA design

The transEDIT-dual CRISPR lentiviral library was designed using the most comprehensive algorithm for gRNA design available. The CRoatan algorithm (Erard, Knott and Hannon) was developed by a random-forest-based gRNA prediction tool (trained on numerous exiting algorithms and comparative bench-marking) (Doench, et al., Chari, et al., Bae et al. and Shi et al.) to create novel and superior gRNA designs. By using the CRoatan algorithm, two independent gRNAs were paired to optimize their dual activity to create deleterious mutations in coding genomic DNA.
   

CRoatan Algorithm

 

Genetic screens have played a fundamental role in charting genotype-phenotype interaction maps for variety of organisms. However, confounding factors, such as non-uniformity in the efficacies of targeting molecules, have limited the depth to which data from such studies can be interpreted. These problems can be mitigated by developing experimentally validated algorithms for selecting potent guide sequences. Approaches have been applied for selecting Cas9 guide RNAs (gRNAs) for use with the Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) system, where large gRNA potency datasets were used to train prediction algorithms. Cas9 induced double-strand-breaks (DSBs) leave a genomic scar whose characteristics determine the phenotypic consequences of targeting a locus.  Frame-shift-mutations (FSMs) are more likely to induce nonsense-mediated mRNA decay or production of truncated non-functional proteins. Deep sequencing of genomic scars has revealed that homologous recombination (HEJ) contributes to repair of Cas9 cleavage events. Here the frequencies of specific repair-resolutions are dependent on the length, GC content and distance from the cut site of the two DSB-flanking homologous loci, suggesting FSM-likelihoods can be estimated.

Finally, gRNAs that focus Cas9 to conserved domains provide a greater probability of phenotypic impact, likely because targeting these regions has a greater potential to disrupt protein function. For gRNA delivery, we have modified a cloning strategy that allows simultaneous expression of two independent gRNAs from a single vector construct, and we have further developed a computational algorithm to optimize guide pairings. We have used a machine-learning approach, as well as other strategies, to optimize the efficiency of gRNAs for CRISPR screens and have constructed a genome-wide, sequence-verified, arrayed CRISPR library. By conducting parallel loss-of-function screens, we compare our approach to existing gRNA design and expression strategies. Our studies demonstrate the significant superiority of this library relative to currently available reagents.
  • Nucleotide to nucleotide interactions selected to support more efficient gRNA binding to the genomic DNA (Doench et al.)
  • Sequence optimization to enable more effective Cas9 binding (Chari et al.)
  • Identification of micro-homology regions that ensure a greater chance of frame-shift mutations (Bae et al.)
  • Identification of conserved amino acid domains to enable targeting of regions more likely to disrupt protein function (Shi et al.)

 
Figure showing design and construction of the CRoatan Algorithm and transEDIT-dual constructs, integrating both existing and novel effector-selection and effector-expression strategies to create a potent gRNA/Cas9 CRISPR knockout system.