Genomic Annotation System Prioritizing Disease Mutations via PIN Rank
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Solution Overview
Problem
Current DNA sequencing technologies generate millions of genetic variants, but interpreting these variants, especially noncoding ones, poses a significant challenge due to computational burden and complexity, limiting their application in understanding common diseases and disease predisposition.
Innovation Solution
A computer-based genomic annotation system using a PIN Rank algorithm to prioritize disease-causing mutations by generating a weighted genetic network, assigning importance scores, and predicting disease risk based on centrality scores and functionality percentages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If high-throughput DNA sequencing is used to identify genetic variants, then the quantity of variants identified increases, but the computational burden and complexity of interpreting these variants increases
Solution Approach 1:
The patent segments the interpretation of genetic variants by prioritizing them into different categories (e.g., high priority, medium priority, low priority) based on their likelihood of being disease-causing. This segmentation allows researchers to focus computational resources and analysis efforts on the most promising variants first, rather than attempting to interpret all variants simultaneously, thereby reducing the overall computational burden while maintaining comprehensive coverage.
Solution Approach 2:
The patent applies preliminary filtering and prioritization actions to genetic variants before full interpretation. By pre-processing variants to identify and rank those most likely to be pathogenic based on initial criteria (such as frequency, location, and predicted impact), the system prepares the data in advance for more focused analysis, reducing the complexity of subsequent interpretation steps.
2Measurement precision
If comprehensive variant interpretation is performed to identify rare disease-causing mutations, then the accuracy of disease association increases, but the time and computational resources required increase
Solution Approach 1:
The patent performs preliminary prioritization of variants based on multiple criteria including allele frequency, genomic location, predicted functional impact, and inheritance patterns. This preliminary action identifies a subset of high-priority variants that are most likely to be disease-causing, allowing researchers to achieve accurate disease association identification without performing exhaustive interpretation on all variants, thereby reducing the time required.
Solution Approach 2:
The patent applies different levels of interpretation depth to different variants based on their priority classification. High-priority variants receive more comprehensive and detailed analysis, while lower-priority variants receive streamlined interpretation. This local quality approach ensures that computational resources and time are concentrated on variants where accurate interpretation has the greatest impact on disease association identification.
3Device complexity
If focus is placed on non-synonymous coding variants in single genes, then the computational burden is reduced, but the ability to address locus and allelic heterogeneity in common diseases is limited
Solution Approach 1:
The patent creates a universal prioritization framework that can handle multiple types of variants (coding and noncoding, rare and common) and multiple disease models (monogenic and polygenic) within a single system. The same prioritization algorithms and criteria can be applied across different gene sets and disease contexts, allowing the system to address locus and allelic heterogeneity in common diseases while maintaining computational efficiency through standardized multi-functional analysis.
Solution Approach 2:
The patent extends the analysis from single genes to multiple genes and pathways simultaneously by incorporating gene-gene interaction networks and pathway enrichment analysis into the prioritization framework. This adds a dimensional aspect to the analysis, allowing the system to capture the complexity of common disease heterogeneity through multi-gene and multi-pathway relationships while maintaining computational feasibility through efficient algorithms.
Data Source
AI summary
A computer-based genomic annotation system, including a database configured to store genomic data, non-transitory memory configured to store instructions, and at least one processor coupled with the memory, the processor configured to implement the instructions in order to implement an annotation pipeline and at least one module filtering or analysis of the genomic data.