Interactive Genome Dashboard for Variant Prioritization
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Solution Overview
Problem
Current whole exome sequencing (WES) technologies face challenges in efficiently interpreting and filtering vast amounts of genetic variant data to identify disease-causing variants, requiring extensive bioinformatics expertise and resulting in a bottleneck in clinical genetics due to static reports that do not easily accommodate new symptoms or genotype-phenotype associations.
Innovation Solution
A genome system with an interactive dashboard that uses machine learning to match phenotype keywords with gene variants, applying filters and generating diagnoses based on user input, allowing for dynamic updating and prioritization of genetic variants associated with phenotypes, and presenting sortable lists of clinical evidence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If whole exome sequencing generates comprehensive genetic variant data, then diagnostic coverage is improved, but data complexity and analysis difficulty increase
Solution Approach 1:
The system extracts and prioritizes only the most relevant genetic variants from the comprehensive sequencing data by matching against phenotype keywords and applying filters, separating useful diagnostic information from the bulk of irrelevant data
Solution Approach 2:
The interactive dashboard serves as an intermediary tool between the complex sequencing data and the clinician, providing automated variant interpretation and presentation while allowing manual refinement through filters and keyword input
2Ease of manufacture
If static reports are used for variant interpretation, then report generation is simplified, but adaptability to new symptoms and associations is reduced
Solution Approach 1:
The system transitions from static reports to a dynamic interactive dashboard that automatically updates variant interpretations when new phenotype keywords or associations are added, allowing the analysis to adapt in real-time to new clinical information
Solution Approach 2:
The dashboard serves multiple functions including automated variant matching, manual filtering, evidence review, and dynamic reanalysis, making the system adaptable to various clinical scenarios and new discoveries without requiring separate analysis pipelines
3Measurement precision
If extensive bioinformatics expertise is required for variant analysis, then analysis precision is improved, but ease of operation decreases
Solution Approach 1:
The system performs self-service variant interpretation by automatically matching genetic variants to phenotype keywords and generating prioritized lists without requiring user expertise in bioinformatics, while still maintaining high analysis precision through automated algorithms
Solution Approach 2:
The interactive dashboard provides feedback to users by presenting variant lists with supporting evidence and allowing manual adjustment through filters, enabling non-experts to refine results based on clinical judgment without needing to understand the underlying bioinformatics complexity
4Reliability
If manual filtering of variants is performed, then diagnostic accuracy is improved, but time consumption increases
Solution Approach 1:
The system performs preliminary automated filtering and prioritization of variants based on phenotype keyword matching before manual review, reducing the initial list size and time required for accurate diagnostic filtering
Solution Approach 2:
The system applies partial automated action for initial variant prioritization while leaving manual filtering for critical decisions, balancing the time investment between automated processing and human expertise to achieve diagnostic accuracy
Data Source
AI summary
A genome system for displaying an interactive genome dashboard is provided herein. The genome system includes processing device having a processor configured to perform machine learning and performing a matching function between phenotypes and gene variants to create gene matches based upon multiple text inputs and genome sequences introduced through the interactive genome dashboard. The processing device includes memory wherein previously generated matches are tagged and stored based upon the multiple text inputs, the genome sequence, and subsequent receipt of user interaction with the generated matches.