Mutation Analysis Ranking Using Multi-Filter Pathogenicity Scoring
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Solution Overview
Problem
Existing systems cannot accurately determine the degree of possibility that a mutation affects the onset or progression of diseases, such as cancer, based solely on mutations recorded in databases, as various factors need to be considered for interpretation.
Innovation Solution
An analysis apparatus and method that evaluates mutations using a filter processing unit to apply multiple conditions and databases, including basic, time-series, database, function prediction, and quality filters, to generate a rank representing the degree of possibility of a mutation being pathogenic.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple filters and databases are used to evaluate mutations, then the accuracy of determining pathogenicity is improved, but the complexity of the analysis system increases
Solution Approach 1:
The analysis system is divided into multiple independent filter modules (basic filter, time-series filter, database filter, function prediction filter, quality filter), each evaluating specific aspects of mutations. This segmentation allows comprehensive pathogenicity assessment while maintaining modular architecture that manages system complexity through clear separation of concerns.
Solution Approach 2:
Multiple filter results are integrated and combined to generate a comprehensive pathogenicity score. The system merges outputs from diverse data sources including COSMIC database, ClinVar database, SIFT, PolyPhen-2, and time-series information into a unified evaluation framework that produces a single prioritized ranking.
2Reliability
If comprehensive mutation analysis is performed, then the reliability of disease progression assessment is improved, but the time required for analysis increases
Solution Approach 1:
The system performs preliminary filtering and evaluation through multiple specialized filters before final pathogenicity determination. By pre-processing mutation data through basic filters, quality filters, and database checks before comprehensive analysis, the system prepares data in advance to accelerate the final assessment while maintaining thorough evaluation.
Solution Approach 2:
The system transforms comprehensive multi-dimensional mutation evaluation into a single prioritized score through parameter aggregation. By converting complex multi-factor assessment (including frequency changes over time, database matches, functional predictions) into a unified numerical ranking, the system enables rapid comparison and decision-making while preserving comprehensive analysis reliability.
Data Source
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AI summary
An analysis apparatus is provided that: receives mutant base sequence information representing a mutation state, including a mutation location in a base sequence and the content of the mutation, extracted from genetic information of a sample to be analyzed by sequence alignment; synthesizes scores set depending on whether or not each of a plurality of predetermined conditions is satisfied for each mutation state represented by the received mutant base sequence information; and estimates and outputs a rank representing the degree of possibility of being pathologic for each mutation state based on the scores. Recording information representing which condition among a plurality of the conditions is satisfied in each of the plurality of mutation states may be further generated, and the recording information may be output in association with the rank information.