Off-Target Site Identification via Guide Strand Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current bioinformatics tools fail to identify potential off-target sites for nucleotide-directed nucleases, such as CRISPR/Cas9, due to their inability to consider insertions, deletions, and mismatches between guide strands and genomic sequences, leading to unintended off-target cleavage and genetic modifications.
Innovation Solution
Developed methods and systems for identifying and ranking potential off-target sites by comparing guide strand sequences with genomic sequences, allowing for the selection of better target sites and experimental confirmation, using computer-implemented systems that generate query sequences with variations including insertions, deletions, and mismatches, and provide application-specific primers for testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If partial search methods are used to identify off-target sites, then the search process is simplified and faster, but most or all cleavage sites are failed to be located
Solution Approach 1:
The patent segments the guide strand sequence into multiple positions (e.g., positions 1-20) and evaluates each position independently for its contribution to off-target binding. This allows the system to systematically assess all possible mismatch configurations without performing an exhaustive brute-force search of all genomic sequences, thereby maintaining search speed while improving identification completeness.
Solution Approach 2:
The patent performs preliminary evaluation of guide strand positions and mismatch tolerance before conducting the actual off-target search. By pre-determining which positions are more tolerant of mismatches (e.g., positions farther from the PAM site), the system can prioritize search regions and reduce the computational burden, thus maintaining speed while improving completeness.
2Measurement precision
If exhaustive search methods are used to identify all off-target sites, then identification completeness is improved, but the search process becomes more complex and time-consuming
Solution Approach 1:
The patent applies different evaluation criteria to different regions of the guide strand. Positions closer to the PAM site are evaluated with stricter mismatch tolerance, while positions farther away are evaluated with more lenient criteria. This localized quality assessment allows the system to achieve comprehensive identification without uniformly applying complex evaluation to all positions, thus reducing overall computational complexity.
Solution Approach 2:
The patent dynamically adjusts mismatch tolerance parameters based on the position within the guide strand and the specific sequence context. Rather than using a fixed threshold for all positions, the system modifies parameters such as maximum allowed mismatches and mismatch weightings based on local sequence properties, enabling exhaustive identification with optimized computational complexity.
3Reliability
If guide strands with higher specificity are selected, then off-target cleavage is reduced, but the selection process becomes more difficult and requires more comprehensive testing
Solution Approach 1:
The patent implements a feedback mechanism where the off-target prediction results are fed back into the guide strand selection process. The system ranks potential guide strands based on their predicted off-target profiles and provides this information to the user, allowing iterative optimization of guide strand selection without requiring exhaustive experimental testing of all possible guides.
Solution Approach 2:
The patent performs preliminary computational assessment of guide strand specificity before experimental validation. By predicting off-target sites in silico and ranking guide strands based on these predictions, the system pre-filters the search space, making the subsequent experimental selection process easier and more efficient while maintaining high specificity requirements.
Data Source
AI summary
Methods and systems for searching genomes for potential nucleotide-guided nuclease off-target sites are provided. Also provided are methods of searching genomes for potential off-target deadCas9 binding sites. In some embodiments, the methods include ranking the potential off-target sites based on the number and location of mismatches, insertions, and/or deletions in the DNA, RNA, or DNA/RNA guide sequence relative to the genomic DNA sequence at a putative target site in the genome, allowing the selection of better target sites and/or experimental confirmation of off-target sites.