Guide RNA
gRNAAlso known as: sgRNA, single guide RNA
A short synthetic RNA molecule that directs CRISPR-associated nucleases to a specific genomic target through Watson-Crick base pairing with the target DNA.
Guide RNA is a programmable RNA molecule, typically 20 nucleotides in its targeting region, that forms a complex with a Cas nuclease and directs it to a complementary DNA sequence for site-specific cleavage or modification 1.
How It Works
In the engineered CRISPR-Cas9 system, the guide RNA is a single chimeric molecule (sgRNA) that fuses two natural components: the CRISPR RNA (crRNA), which contains the 20-nucleotide spacer complementary to the target, and the trans-activating crRNA (tracrRNA), which forms a scaffold structure recognized by Cas9. The sgRNA-Cas9 complex scans genomic DNA for sequences matching the spacer that are adjacent to a protospacer adjacent motif (PAM, typically NGG for SpCas9).
Guide RNA design involves multiple considerations. The 20-nucleotide spacer must be unique enough to avoid off-target cleavage at similar genomic sites. GC content between 40-70% generally yields optimal activity. The spacer sequence should not contain runs of four or more thymines, which act as termination signals for RNA polymerase III promoters (U6, H1) commonly used to express sgRNAs. Secondary structure in the spacer region can reduce Cas9 loading efficiency.
Multiplexed guide RNA expression enables simultaneous editing at multiple loci, combinatorial gene knockouts, and large-scale genetic screens. Arrays of sgRNAs can be expressed from a single transcript and processed by Csy4 or tRNA-based systems into individual functional guides.
Computational Considerations
Machine learning models such as DeepCpf1 and Rule Set 2 predict guide RNA on-target activity from sequence composition, position-specific nucleotide preferences, and thermodynamic features 2. Off-target scoring algorithms align guide sequences against whole genomes, weighting mismatches by position and type to estimate the risk of unintended editing events.
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Deep learning models predict guide RNA activity scores and off-target profiles from sequence features, enabling high-confidence guide selection for genome editing experiments.