CRISPR Guide Selection via Transcript and Epigenetic Filtering
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Solution Overview
Problem
Current CRISPR guide selection methods face challenges in identifying effective guide sequences that induce frameshift mutations with low off-target effects, as they often rely on random selection processes rather than systematic evaluation of genetic and epigenetic criteria.
Innovation Solution
The technology employs a method that uses transcript annotations, knockout models, and predictive tools like FORECasT and Azimuth to identify and rank CRISPR-Cas guide sequences based on criteria such as transcript support, epigenetic features, and predicted frameshift efficiency, ensuring targeted and efficient gene knockout with minimal off-target activity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If random selection of guide sequences is used, then the selection process is simple, but the effectiveness of inducing frameshift mutations is low and off-target effects increase
Solution Approach 1:
The patent applies preliminary action by pre-evaluating and ranking guide sequences based on multiple criteria (transcript support, epigenetic features, predicted frameshift efficiency) before actual CRISPR experiments. This preliminary ranking system allows researchers to select the most effective guides in advance, improving mutation induction effectiveness while providing a systematic framework that manages complexity through automation.
Solution Approach 2:
The patent employs parameter changes by evaluating guide sequences across multiple parameters including transcript support levels, epigenetic features (DNase hypersensitivity, histone modifications), and predicted frameshift efficiency. By changing and optimizing across these multiple parameters simultaneously, the system identifies guides that maximize on-target effectiveness while minimizing off-target effects.
2Object-affected harmful factors
If comprehensive evaluation criteria are used to select guide sequences, then off-target effects are minimized, but the selection process becomes more complex
Solution Approach 1:
The patent applies segmentation by dividing the guide sequence evaluation into distinct modular criteria: transcript support evaluation, epigenetic feature assessment, predicted frameshift efficiency scoring, and off-target effect prediction. Each criterion can be independently calculated and combined, allowing comprehensive evaluation while managing complexity through modular, systematic assessment of each factor separately.
Solution Approach 2:
The patent uses computational prediction tools and scoring systems as intermediaries to evaluate multiple complex factors. These intermediary algorithms process transcript annotations, epigenetic data, and sequence features to generate composite scores that predict guide effectiveness and off-target potential, simplifying the integration of multiple evaluation criteria into actionable recommendations.
3Reliability
If multiple filtering criteria are applied to rank coding sequences, then biological relevance is improved, but the number of selected guides decreases
Solution Approach 1:
The patent applies dynamics by implementing a tiered filtering system where criteria can be applied at different stringency levels. Researchers can adjust the number of filters applied or the stringency of each filter based on their specific needs. The system dynamically balances biological relevance requirements against the desired number of candidate guides, allowing flexible optimization for different experimental contexts.
Data Source
AI summary
A system for selecting CRISPR guides for knocking out one or more target genes in a target cell from a multiplicity of candidate guides comprises a memory and a processor. The processor determines whether the candidate guide meets a plurality of thresholds. The thresholds are associated with: a transcript support level; targeting a consensus sequence of a target gene; which exon of the target gene is targeted; targeting of a primary transcript, targeting of a common isoform; a precomputed prediction of editing outcomes; mapping to an expressed sequence; fraction of gene expression attributable to targeted transcripts; a common SNP overlap threshold; which exon of the target gene is targeted; overlap of a selected guide; predicted frameshift percentage; maximum and minimum GC content; off target score; where a coding sequence is targeted. In response to meeting the thresholds, the processor selects the candidate guide as a selected CRISPR guide.