CNN Methylation Pattern Detection for CRISPR Genomic Editing
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Solution Overview
Problem
Current methods lack a reliable and universal tool to detect and track changes in methylation patterns caused by CRISPR technology, posing risks due to unpredictable phenotypic outcomes and unintended deregulation of regulatory elements.
Innovation Solution
A system utilizing convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) to analyze whole-genome bisulfite sequencing data, generating image representations of methylation variations and determining the probability of CRISPR-edited methylation regions by training on CRISPR-edited genome data and augmented images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If CRISPR gene editing is performed to treat genetic diseases, then genomic edits with high precision and efficiency are achieved, but unintended changes in methylation patterns occur causing unpredictable phenotypic outcomes
Solution Approach 1:
The patent implements a feedback mechanism by using CNN-based detection to analyze methylation patterns in CRISPR-edited genomes. The system processes whole-genome bisulfite sequencing data, generates image representations of methylation variations, and provides scores indicating the probability of CRISPR-mediated changes. This feedback loop enables researchers to identify and correct unintended methylation alterations, thereby improving the reliability and predictability of phenotypic outcomes while maintaining high genomic editing precision.
2Ease of operation
If traditional methods are used to detect CRISPR edits, then the process is simple, but the detection of methylation changes is unreliable and lacks universality
Solution Approach 1:
The patent replaces traditional mechanical or manual detection methods with an automated computational system based on convolutional neural networks. The system automatically processes whole-genome bisulfite sequencing data, transforms it into image representations, and applies trained CNN models to detect CRISPR-mediated methylation changes. This substitution maintains ease of operation through automated processing while dramatically improving measurement precision and reliability of methylation change detection.
3Measurement precision
If comprehensive analysis of methylation patterns is performed across the genome, then detection accuracy improves, but computational complexity and data processing requirements increase
Solution Approach 1:
The patent applies dimensionality change by transforming one-dimensional genomic methylation data into two-dimensional image representations. Whole-genome bisulfite sequencing data is processed to create visual plots showing methylation variations across genomic regions. These image representations can be analyzed by CNNs that have been trained on similar images, enabling comprehensive genome-wide analysis with improved detection accuracy while managing computational complexity through established image processing techniques.
Data Source
AI summary
A system, method, and computer-readable medium for detecting a CRISPR-edited genome are disclosed. Certain embodiments of the system may include one or more processors configured to receive sequence data of a genome; generate an image representation of the sequenced data, the image being a plot of methylation variations as a function of methylation locations in the genome; apply the generated image representation to a trained convoluted neural network (CNN); generate, using the CNN, a score indicative of a probability that the genome was CRISPR-edited; and determine, based on the score, whether the genome contains a CRISPR-edited methylation region. A corresponding method and computer-readable medium are also provided.