Genomic Annotation System Using PIN Rank Algorithm
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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 heritability.
Innovation Solution
A computer-based genomic annotation system using a PIN Rank algorithm to prioritize disease-causing mutations by generating a weighted genetic network, calculating centrality scores, and predicting disease risk based on genetic variant data, making variant interpretation more accessible and efficient.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If high-throughput DNA sequencing is used to identify millions of genetic variants, then the quantity of genomic data increases, but the computational burden and complexity of interpreting these variants increases
Solution Approach 1:
The patent segments the interpretation process into multiple annotation levels: Level 1 annotates variants with basic genomic context, Level 2 adds functional predictions, Level 3 incorporates regulatory information, and Level 4 provides disease association data. This segmentation allows the system to handle millions of variants systematically by processing them in organized tiers rather than as a monolithic complex task.
Solution Approach 2:
The patent introduces intermediary annotation layers that mediate between raw sequencing data and final disease interpretation. These intermediate annotation levels serve as bridges, transforming raw variant calls into functionally annotated data that can be systematically processed and interpreted, thereby reducing the direct computational complexity of interpreting millions of variants.
2Reliability
If focus is placed on noncoding variants to address heritability in common diseases, then the completeness of disease explanation improves, but the difficulty of interpretation increases
Solution Approach 1:
The patent performs preliminary annotation of noncoding variants before disease association analysis. By pre-annotating variants with functional predictions, regulatory element memberships, and disease relevance data in organized levels, the system prepares the data structure in advance, making subsequent interpretation and disease association testing more manageable and less computationally intensive.
Solution Approach 2:
The patent adds multiple dimensions of annotation to noncoding variants, transforming them from simple genomic coordinates to multi-dimensional data objects that include functional predictions, regulatory context, evolutionary conservation, and disease associations. This dimensional enrichment allows systematic processing and interpretation of noncoding variants that would otherwise be intractable.
3Measurement precision
If computational resources are allocated to interpret all variants comprehensively, then the accuracy of disease risk prediction improves, but the time required for analysis increases
Solution Approach 1:
The patent segments variant interpretation into prioritized levels, allowing users to allocate computational resources by selecting the number of annotation levels to process. This segmentation enables trade-offs between analysis depth and time, where Level 1 provides basic annotation with minimal computational cost, while Level 4 provides comprehensive disease association data requiring more resources but delivering higher prediction accuracy.
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.