Graph Database Genetic Analysis for Flexible Mutation Queries
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Solution Overview
Problem
Existing genetic screening technologies face challenges in efficiently analyzing and reporting genetic data due to the vast volumes of data and the need for exhaustive index table lookups, limiting the ability to accommodate novel mutations or associations, and requiring extensive re-design for new information integration.
Innovation Solution
A graph database system is used to individually represent entities like mutations, patients, and clinical information as nodes, allowing flexible addition of new entities and relationships without disrupting the system, enabling rapid querying and reporting of genetic data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional database schema with indexed tables is used, then data storage and comparison can be performed, but the run time for queries becomes proportional to the amount of data in the tables
Solution Approach 1:
The patent segments the monolithic database into multiple data shards distributed across different servers. Each shard contains a subset of the total data, allowing queries to be parallelized across shards. This reduces the time complexity from O(n) for a single large table to O(n/p) where p is the number of parallel processing units, effectively decoupling query time from total data volume.
Solution Approach 2:
The patent introduces a new dimensional approach by implementing a distributed architecture with multiple data shards across different servers rather than relying on a single-dimensional indexed table. This multi-dimensional distribution allows the system to handle large data volumes without proportionally increasing query time, as queries can traverse multiple dimensions (shards, servers, nodes) in parallel.
2Stability of the object's composition
If databases are designed with fixed schemas prior to use, then data structure is stable, but the system cannot accommodate novel mutations or associations without re-design
Solution Approach 1:
The patent implements dynamic schema evolution capabilities where the database structure can adapt to new mutations and associations without complete re-design. The system allows for incremental schema changes, adding new fields, data types, and relationships as novel genetic information is discovered, while maintaining backward compatibility with existing data and queries.
Solution Approach 2:
The patent creates a universal database architecture that can handle multiple types of genetic data (mutations, associations, phenotypes, genotypes) within a single flexible framework. The system uses polymorphic data structures and inheritance hierarchies that allow new entity types to be integrated without disrupting existing functionality, enabling the database to serve multiple purposes simultaneously.
3Reliability
If exhaustive index table lookups are performed for each comparison, then complete data coverage is achieved, but the time required for analysis and reporting increases
Solution Approach 1:
The patent implements preliminary indexing and pre-computation of common query patterns. Frequently accessed data relationships are pre-calculated and stored in optimized formats, allowing the system to retrieve this information without performing exhaustive lookups during actual analysis. This preliminary preparation maintains complete data coverage while significantly accelerating reporting speed for common queries.
Solution Approach 2:
The patent dynamically adjusts query parameters and optimization strategies based on the specific analysis needs. The system can switch between exhaustive search modes (for complete coverage) and optimized retrieval modes (for speed) depending on the query type, data distribution, and performance requirements. This parameter flexibility allows the system to balance reliability and productivity based on operational context.
Data Source
AI summary
The invention relates to using a graph database in genetic analyses to link mutation data to extrinsic data. Entities such as mutations, patients, samples, alleles, and clinical information are individually represented and stored as nodes and relationships between entities are also individually represented and stored. Each node and relationship can be stored using a fixed-size record and nodes can be flexibly invoked to represent any entity without disrupting the existing data. Systems and methods of the invention may be used for obtaining data representing a mutation in an individual and using a node in a graph database to store a description of the mutation. The node has stored within it a pointer to an adjacent node that provides information about a clinical significance of the variant. The graph database can be queried to provide a report of the clinical significance of the mutation.