Multi-omics Integration for Nonsense Mutation Detection
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Solution Overview
Problem
Current omics analysis methods face challenges in integrating and analyzing large volumes of genomic, transcriptomic, and proteomic data effectively, particularly in cancer diagnosis and therapy, where the presence of mutations in tumor genomes does not necessarily predict gene expression or their effects, and RNomics results lack informative value without contextual data from other omics platforms.
Innovation Solution
A system and method for integrating genomic and transcriptomic data to detect molecular markers, specifically contextualizing patient and tumor-specific mutations with RNA transcription levels, particularly for nonsense mutations in genes associated with malignancies, using an omics record computer system that processes and analyzes genomic and transcriptomic data sets to identify highly transcribed mutated RNA as diagnostic and therapeutic tools.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If high throughput sequence analysis is used to analyze genomic data, then sequencing efficiency is improved, but the ability to predict gene expression and mutation effects remains insufficient
Solution Approach 1:
The patent combines genomic data analysis with RNomics (transcriptomic data) to create an integrated multi-omics analysis system. This merging allows the system to not only sequence DNA efficiently but also analyze RNA expression levels, thereby predicting gene expression and mutation effects that genomic analysis alone cannot provide.
Solution Approach 2:
The patent introduces RNomics data as an intermediary layer between genomic mutations and phenotypic outcomes. By analyzing RNA transcription levels and processing, the system mediates the connection between DNA mutations and their functional effects, providing predictive information about gene expression and mutation impact.
2Measurement precision
If RNomics analysis is performed in isolation, then transcription level data is obtained, but the informative value for cancer diagnosis remains limited
Solution Approach 1:
The patent merges RNomics analysis with genomic analysis and proteomics data to create a comprehensive multi-omics framework. This integration allows transcription level data to be contextualized with mutation information and protein expression, significantly enhancing the diagnostic informative value beyond what RNomics alone can provide.
Solution Approach 2:
The patent creates a multi-functional analysis system that processes multiple types of omics data (genomics, RNomics, proteomics) within a single integrated platform. This universal system can perform various diagnostic functions including mutation detection, expression analysis, and predictive modeling, maximizing the utility of transcription level data.
3Measurement precision
If multiple omics platforms are integrated for comprehensive analysis, then diagnostic accuracy is improved, but data integration complexity increases
Solution Approach 1:
The patent segments the complex multi-omics integration process into distinct analytical modules: genomic data processing, RNomics data processing, proteomics data processing, and integrated analysis. Each module handles specific data types independently before results are synthesized, reducing integration complexity while maintaining comprehensive analysis capability.
Solution Approach 2:
The patent introduces computational algorithms and data normalization procedures as intermediaries that standardize and harmonize data from different omics platforms. These intermediary processing steps convert heterogeneous data formats into a unified analytical framework, simplifying integration while preserving diagnostic accuracy.
Data Source
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AI summary
Contemplated systems and methods integrate genomic/exomic data with transcriptomic data by correlating a cancer associated mutation in the genome/exome with the transcription level of the affected gene carrying the mutation, particularly where the mutation is a 3-terminal nonsense mutation.