Genome Analysis Platform Identifying Deleterious Mutations
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Solution Overview
Problem
Current technologies in Precision Medicine lack advanced tools for analyzing genetic loci and mutations, limiting the ability to provide deep insights for disease diagnosis and treatment, especially in understanding how mutations cause disease and their impact on gene function.
Innovation Solution
The development of the Gene Disease Mutation Analysis Platform (GDMAP) and SpliceCode, which utilize novel statistical graphing approaches and AI/ML systems to analyze genetic features, mutations, and genomes, calculating deleteriousness scores and identifying pathogenic mutations, and SpliceCode identifies genetic elements responsible for splicing, enabling deeper understanding of disease causation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If basic data retrieval and viewing capabilities are used for genetic publications, then ease of operation is improved, but measurement precision and analytical depth are insufficient for advanced precision medicine research
Solution Approach 1:
The system segments the genome analysis process into distinct functional modules: sequence retrieval, mutation identification, similarity scoring, and effect prediction. Each module handles a specific aspect of genome analysis, allowing complex analyses to be broken down into manageable steps that can be executed systematically
Solution Approach 2:
The patent introduces intermediary computational tools and algorithms that bridge raw genomic data and clinical interpretation. These intermediaries include similarity scoring systems, mutation effect predictors, and meta-analysis engines that transform basic sequence data into actionable medical insights without requiring direct complex manual analysis
2Productivity
If comprehensive genome sequencing is performed, then measurement precision of genetic features is improved, but the ability to quickly identify causative mutations and provide clinical insights is limited without advanced analysis tools
Solution Approach 1:
The system performs preliminary actions by pre-processing and annotating genomic sequences with known genetic elements, regulatory regions, and conserved domains before clinical analysis is needed. This pre-characterization allows rapid identification of causative mutations during clinical workflows without requiring de novo analysis of entire genomes
Solution Approach 2:
The patent employs parameter changes by adjusting similarity scoring thresholds, mutation frequency cutoffs, and statistical significance levels to optimize the balance between sensitivity and specificity in mutation identification. These parameter adjustments enable the system to adapt to different clinical scenarios and data quality levels
Data Source
AI summary
Presented herein are methods and systems directed to analysis of features, mutations, and genome sequences. Analysis of genetic features can identify strongly or weakly causative deleterious mutations.