Automated Reversion Mutation Detection via Genomic Variant Feature Comparison
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Solution Overview
Problem
Existing methods for detecting reversion mutations in tumor suppressor genes, such as BRCA1 and BRCA2, are complex, laborious, and inaccurate, hindering informed treatment decisions for cancer patients.
Innovation Solution
An automated method using an extensive variant sequence database to identify, parse, and classify reversion mutations by comparing structural and functional features of variant sequences, enabling accurate detection and classification of reversion mutations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review and curation methods are used to detect reversion mutations, then detection can be performed, but the process becomes complex, laborious, and inaccurate
Solution Approach 1:
The patent replaces manual review and curation processes with an automated computational system that uses algorithms to detect and classify reversion mutations. The system automatically processes genomic sequencing data, identifies variant sequences, determines their structural features, and classifies them as reversion mutations without human intervention, thereby eliminating the complexity and laborious nature of manual methods while maintaining or improving detection accuracy.
Solution Approach 2:
The system enables self-service detection by implementing automated pipelines that independently perform all steps of reversion mutation detection: data processing, variant identification, structural feature analysis, and classification. The computational system serves itself by automatically making decisions about mutation classification based on predefined criteria, eliminating the need for external manual curation and reducing process complexity.
2Reliability
If manual review methods are used for detecting reversion mutations, then detection is possible, but the process is laborious and time-consuming
Solution Approach 1:
The patent replaces time-consuming manual review processes with automated computational algorithms that can process genomic data rapidly. The system automatically executes the entire detection workflow including data processing, variant calling, structural feature determination, and mutation classification, reducing detection time from days or weeks of manual work to hours or minutes of automated processing while maintaining reliable detection results.
Solution Approach 2:
The system performs preliminary automated processing of genomic sequencing data, pre-identifying potential variant sequences and their structural features before final classification. By preparing and organizing the data structure in advance with automated pipelines, the system reduces the time required for actual mutation detection and classification, enabling faster turnaround while ensuring reliable results through systematic preprocessing.
3Ease of operation
If existing detection methods are used, then reversion mutations can be identified, but the accuracy and precision are insufficient for informed treatment decisions
Solution Approach 1:
The patent replaces imprecise manual detection methods with an automated computational system that applies consistent, objective criteria for identifying and classifying reversion mutations. The system uses algorithmic rules to determine structural features and classify mutations, eliminating human error and variability, thereby providing highly accurate detection results that enable healthcare providers to make informed treatment decisions with confidence.
Solution Approach 2:
The system incorporates feedback mechanisms where the automated classification results can be validated and refined. The computational system provides structured output that includes confidence metrics and classification rationale, allowing for verification and adjustment if needed, thereby ensuring high precision in mutation detection while maintaining ease of operation for treatment decision-making.
Data Source
AI summary
Methods for detection and classification of reversion mutations are described. The methods may comprise, for example, receiving sequence data for nucleic acid sequences that reside within one or more gene loci within a subgenomic interval in a sample from a subject; identifying a gene locus of the one or more gene loci for which the gene locus comprises two or more variant sequences; categorizing the two or more variant sequences in the gene locus according to a structural feature or functional effect; comparing the structural features or functional effects of the two or more categorized variant sequences in the gene locus; and classifying the two or more categorized variant sequences in the gene locus based on the comparison, where the classification indicates whether the two or more categorized variant sequences comprise a reversion mutation.