Our platform's CRISPR Guide RNA Designer is an advanced and versatile tool that empowers researchers to design highly specific and efficient guide RNAs (gRNAs) for CRISPR-based genome editing across a wide range of organisms and CRISPR systems. Whether you're working with Cas9, Cas12, Cas13 enzymes, or their various orthologs and engineered variants (including both active and dead forms), our designer accommodates all, including specialized applications like nickases, base editing, and prime editing with additional output metrics tailored to these technologies.

Input Options:

• Organism:

  • Selection Flexibility: Choose any organism for which genomic data is available, from model organisms like Homo sapiens and Mus musculus to less-studied species.

  • Genome Versions: Select specific genome assemblies or versions to ensure compatibility with your research data.

• Target Genes or Sequence:

  • Gene Identification: Input gene names (e.g., "TP53"), accession numbers, or IDs such as Ensembl IDs ("ENSG00000141510") or NCBI Gene IDs ("7157") to automatically retrieve gene sequences.

  • Custom Sequences: Paste or upload custom nucleotide sequences if targeting non-coding regions, regulatory elements, or novel sequences.

• Guide RNA Size:

  • Length Specification: Define the length of the guide RNA (commonly 20 nucleotides for Cas9) to suit the requirements of different CRISPR systems or experimental designs.

• PAM (Protospacer Adjacent Motif):

  • Automatic Selection: Specify the CRISPR nuclease (e.g., SpCas9, SaCas9, AsCas12a), and the appropriate PAM sequence will be automatically applied.

  • Custom PAMs: Manually input custom PAM sequences for engineered nucleases or less common variants.

• Target Region:

  • Region Specification: Focus the guide RNA design on specific exons, introns, promoters, or other genomic regions.

  • Functional Domains: Target regions encoding critical functional domains to maximize the impact of gene disruption or modification.

• Efficiency Score:

  • Threshold Setting: Set minimum acceptable efficiency scores (e.g., scores above 50) to filter for guides predicted to have high activity.

  • Scoring Algorithms: Utilize various prediction models (e.g., CFD score, Doench score) to assess guide RNA efficiency.

• Repair Profile Prediction:

  • Editing Outcomes: For applications like base editing and prime editing, obtain predictions of repair outcomes, including indel patterns and base conversion rates.

  • Customization: Adjust parameters influencing repair profiles, such as donor template sequences for prime editing.

Output Options:

• Rank:

  • Effectiveness Ordering: Guides are ranked based on a composite score reflecting on-target efficiency, specificity, and other relevant metrics.

  • Prioritization: Easily identify top candidates for experimental validation.

• Guide Sequence:

  • Nucleotide Details: Provides the exact sequence of the guide RNA, essential for synthesis and cloning.

• PAM Sequence:

  • 3' or 5' PAM Identification: Indicates the PAM sequence adjacent to the guide RNA target site, which is crucial for CRISPR nuclease binding.

• Location:

  • Genomic Coordinates: Specifies the exact chromosomal location (e.g., chr17:7,571,720-7,571,739) of the target site.

  • Contextual Information: Includes information about the genomic context, such as exon number or regulatory region.

• Strand:

  • Targeting Strand: Indicates whether the guide RNA targets the sense (+) or antisense (-) DNA strand.

• On-target Score:

  • Efficiency Prediction: A quantitative score predicting the guide RNA's activity at the intended site, helping assess the likelihood of successful editing.

• Off-targets:

  • Specificity Assessment: Lists the number of potential off-target sites with varying degrees of mismatches (e.g., "0,0,1,3"), providing insight into specificity.

    • Interpretation Example: "0,0,1,3" means 0 off-targets with 0 or 1 mismatch, 1 off-target with 2 mismatches, and 3 off-targets with 3 mismatches.

  • Off-target Locations: Optionally provides genomic locations of potential off-targets for further analysis.

• GC Content (%):

  • Sequence Composition: Indicates the percentage of guanine (G) and cytosine (C) nucleotides, affecting guide RNA stability and efficiency.

• Self-complementarity:

  • Secondary Structure Potential: Measures the propensity of the guide RNA to form hairpins or other secondary structures that could reduce efficacy.

• Additional Metrics for Specialized Applications:

  • Nickase Pairing Guides:

    • Dual Targeting: For Cas9 nickase applications, provides paired guides that target opposite strands offset by a specific distance.

  • Base Editing Metrics:

    • Editing Window Prediction: Indicates the nucleotide positions within the guide sequence where base editing is most efficient.

    • Substrate Compatibility: Notes suitability for specific base editors (e.g., cytidine deaminases, adenine deaminases).

  • Prime Editing Designs:

    • pegRNA Construction: Provides primer extension guide RNA (pegRNA) sequences, including spacer, scaffold, reverse transcriptase template, and primer binding site.

    • Predicted Editing Outcomes: Estimates the types and frequencies of edits (insertions, deletions, substitutions) achievable with each design.

Comprehensive Support for CRISPR Systems:

• Cas Nuclease Variants:

  • Cas9 Family: Supports Streptococcus pyogenes Cas9 (SpCas9), Staphylococcus aureus Cas9 (SaCas9), and others.

  • Cas12 and Cas13 Families: Enables guide design for systems like Cas12a (Cpf1) and Cas13, accommodating their unique PAM requirements and cleavage patterns.

  • Dead and Nickase Versions: Facilitates designs for catalytically inactive (dead) nucleases used in gene regulation and nickases for single-strand cuts.

• Advanced Editing Technologies:

  • Base Editing:

    • Precision Editing: Design guides compatible with base editors for targeted nucleotide conversions without double-strand breaks.

    • Editor Compatibility: Indicates which base editors (e.g., BE3, ABE) are suitable for each guide.

  • Prime Editing:

    • Versatile Modifications: Create pegRNAs for introducing targeted insertions, deletions, and all types of point mutations.

    • Design Complexity: Accounts for factors like primer binding site length and reverse transcriptase template design.

Benefits and Applications:

• Customization and Precision:

  • Tailor guide RNA designs to specific experimental needs, ensuring high specificity and efficiency.

• Efficiency and Time-Saving:

  • Automates complex calculations and predictions, reducing the time from design to experimentation.

• Comprehensive Data Analysis:

  • Provides in-depth information on each guide candidate, allowing informed decision-making and prioritization.

• Educational and Research Utility:

  • Useful for both educational purposes and cutting-edge research, supporting applications from gene knockout studies to therapeutic development.

By integrating a wide array of input parameters and delivering detailed output metrics, our CRISPR Guide RNA Designer stands as an indispensable tool for researchers aiming to harness the full potential of CRISPR technology. Its ability to accommodate various CRISPR systems and editing strategies makes it a one-stop solution for all genome editing needs, facilitating advancements in genetics, molecular biology, and biotechnology.