RNA Similarity Analysis via Graph Structure Reconstruction
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Solution Overview
Problem
Current methods for analyzing the similarity of RNA secondary structures are subjective, inefficient, and lack the ability to conveniently, intelligently, efficiently, quickly, and intuitively calculate similarities between RNA molecules.
Innovation Solution
A graph calculation method that converts RNA sequence data into structure graphs, analyzes similarity between these graphs, determines the number of base constituent structures, reconstructs higher-order graphs, and combines these similarities to obtain a final similarity score.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If biologists use naked eye observation to judge RNA secondary structure similarity, then the analysis can be performed subjectively, but the process is inefficient and lacks objective calculation capability
Solution Approach 1:
The patent replaces the mechanical/subjective naked-eye observation method with an automated computational system. It converts RNA secondary structures into graph data structures and uses algorithmic similarity calculation (graph isomorphism testing, graph kernels, and graph neural networks) to objectively compute similarity scores, thereby eliminating manual observation while dramatically improving analysis efficiency and scalability.
2Extent of automation
If traditional algorithms like tree structure or wavelet analysis are used to judge RNA similarity, then some computational capability is provided, but the similarity calculation cannot be performed conveniently, intelligently, efficiently, quickly, and intuitively
Solution Approach 1:
The patent segments the RNA secondary structure into fundamental graph elements (nodes representing bases, edges representing base pairs and structural relationships). By decomposing the complex RNA structure into these discrete graph components, the system enables systematic and automated similarity calculation through graph theoretical operations, making the process both convenient and intuitive while maintaining high computational efficiency.
3Measurement precision
If graph-based similarity analysis is implemented, then objective and efficient RNA similarity calculation is achieved, but the system complexity increases
Solution Approach 1:
The patent employs a nested graph representation where the RNA secondary structure is encoded as a graph with nested hierarchical levels. The graph structure embeds multiple levels of structural information (base-level nodes, stem-loop motifs, and higher-order structural patterns) within a unified framework. This nesting approach enables precise similarity measurement at multiple scales while managing system complexity through hierarchical organization rather than requiring entirely separate analysis systems.
Data Source
AI summary
A graph calculation method of RNA similarity analysis, an apparatus, a device, and a medium are provided. The method includes: converting sequence data of a looked-up RNA into a looked-up RNA structure graph; obtain a first similarity between the looked-up RNA structure graph and a target RNA structure graph; obtaining a second similarity based on the number of base constituent structures in the looked-up RNA structure graph and the number of base constituent structures in the target RNA structure graph; reconstructing the looked-up RNA structure graph based on the base constituent structures in the looked-up RNA structure graph to generate a looked-up RNA higher-order graph; and analyzing similarity between the looked-up RNA higher-order graph and a target RNA higher-order graph to obtain a third similarity; and obtaining a final similarity between the looked-up RNA and the target RNA based on the first similarity, the second similarity, and the third similarity.