High-Dimensional Sequence Read Analysis for Disease Prediction
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Solution Overview
Problem
Modern sequencing technologies generate vast amounts of nucleic acid data, but efficiently extracting relevant information for disease diagnosis and prognosis remains challenging due to the high dimensionality and irrelevance of much of the data.
Innovation Solution
A method is disclosed for analyzing sequence reads by identifying regions of low variability in a reference genome, selecting a training set of sequence reads, determining parameters reflecting differences between healthy and diseased subjects, and predicting disease likelihood based on these parameters using a test set of sequence reads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If next-generation sequencing technologies are used to obtain entire human genome sequence reads, then comprehensive genetic information is obtained, but the data volume becomes excessively large and contains much irrelevant information for disease diagnosis
Solution Approach 1:
The patent segments the entire human genome into multiple genomic regions and selectively analyzes only those regions that are relevant to specific disease conditions. This segmentation approach allows the system to divide the overwhelming 3 billion base pairs into manageable, disease-specific subsets, thereby obtaining comprehensive genetic information for disease diagnosis while filtering out irrelevant genomic data.
Solution Approach 2:
The patent extracts and focuses on specific genomic regions that are known to be associated with particular disease conditions, separating these relevant regions from the rest of the genome. By taking out only the disease-relevant portions of the genome for analysis, the system achieves comprehensive disease-related genetic information while eliminating the vast amount of irrelevant genomic information.
2Reliability
If comprehensive genome sequencing is performed, then complete genetic data is obtained, but processing time and computational resources increase significantly
Solution Approach 1:
The patent divides the genome into multiple regions and performs sequencing and analysis on only those regions relevant to the disease condition being investigated. This segmentation maintains reliability by ensuring all disease-relevant genetic data is captured, while significantly reducing processing time by excluding irrelevant genomic regions from the sequencing and analysis pipeline.
Solution Approach 2:
The patent performs preliminary identification of disease-relevant genomic regions before conducting full sequencing. By pre-defining which genomic regions to analyze based on disease associations, the system prepares the analysis framework in advance, ensuring complete capture of relevant genetic data while avoiding the time-consuming processing of unrelated genomic regions.
3Measurement precision
If all sequence reads are analyzed for disease diagnosis, then no useful information is missed, but the complexity of data processing increases dramatically
Solution Approach 1:
The patent segments the analysis process to focus on specific genomic regions associated with disease conditions rather than analyzing all sequence reads across the entire genome. This segmentation maintains measurement precision by ensuring all disease-relevant variants are detected, while reducing data processing complexity by limiting the analysis scope to predetermined disease-associated regions.
Solution Approach 2:
The patent applies different analysis strategies to different genomic regions based on their disease relevance. Disease-associated regions receive detailed, high-precision analysis to ensure accurate detection, while non-disease regions are either analyzed with reduced complexity or excluded entirely. This local quality approach maintains detection accuracy for critical regions while managing overall processing complexity.
Data Source
AI summary
A system, method and computer program product for analyzing data of high dimensionality (e.g., sequence reads of nucleic acid samples in connection with a disease condition) are provided.