Unsupervised ML for CRISPR Loci Classification
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Solution Overview
Problem
Current methods for identifying novel CRISPR effector elements are limited by biased classification approaches and lack comprehensive characterization of CRISPR loci, leading to the potential oversight of novel functional roles and effectors.
Innovation Solution
An unsupervised machine learning method, including hierarchical clustering and neural network models, is employed to classify CRISPR loci and identify novel effector elements by analyzing sequence and domain similarities, enabling the discovery of new CRISPR loci and effectors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If biased classification approaches are used to identify CRISPR effector elements, then the classification process is simplified, but novel functional roles and effectors are overlooked
Solution Approach 1:
The patent inverts the traditional biased classification approach by implementing an unsupervised machine learning system that allows data to self-organize into clusters without pre-defined categories. This inversion enables novel CRISPR loci and effector elements to emerge naturally from the data structure rather than being forced into existing classification frameworks, thereby preventing loss of novel functional information while maintaining systematic organization.
Solution Approach 2:
The patent introduces unsupervised machine learning algorithms as an intermediary between raw CRISPR locus data and classification outcomes. This intermediary layer processes sequence and domain similarity data through hierarchical clustering and neural network models, enabling objective identification of novel effector elements without human bias while still producing structured classification results.
2Measurement precision
If comprehensive characterization of CRISPR loci is performed, then novel effector elements are identified, but the analysis time and computational resources increase
Solution Approach 1:
The patent performs preliminary actions by pre-computing sequence similarities and domain similarities for all CRISPR locus elements before the actual classification process. This preliminary processing creates a structured similarity matrix that can be efficiently queried during analysis, enabling comprehensive characterization of novel effector elements without requiring exhaustive real-time comparisons, thus reducing analysis time while maintaining high measurement precision.
Solution Approach 2:
The patent replaces traditional mechanical sequence-by-sequence comparison methods with machine learning-based similarity assessment. By using unsupervised learning algorithms that leverage pre-computed similarity metrics and hierarchical clustering, the system achieves comprehensive CRISPR loci characterization with significantly reduced computational overhead and analysis time compared to brute-force mechanical comparison approaches.
Data Source
AI summary
Embodiments disclosed herein provide methods for identifying new CRISPR loci and effectors, as well as different CRISPR loci combinations found in various organisms. Class-II CRISPR systems contain single-gene effectors that have been engineered for transformative biological discovery and biomedical applications. Discovery of additional single-gene or multicomponent CRISPR effectors may enhance existing CRISPR applications, such as precision genome engineering. Comprehensive characterization of CRISPR-loci may identify novel functional roles of CRISPR loci enabling new tools for biomedicine and biological discovery. CRISPR loci have enormous feature complexity, but classification of CRISPR loci has been focused on a small fraction of highly abundant features. Increased genome sequencing has enhanced the sampling of this feature complexity.