Extensible Dataset Mapping to Relational Database Schemas
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Solution Overview
Problem
The analysis of variant call format (VCF) files in bioinformatics is lengthy and resource-intensive due to the need to store and process genetic data variations alongside a reference genome, often resulting in duplications and overlap, which complicates the mapping to a relational database schema.
Innovation Solution
A method is described where metadata and content lines from VCF files are mapped to metadata and content tables in a relational database schema, utilizing prefix parameters to determine table names, column numbers, and data types, with dynamic and static table structures to efficiently store and analyze gene sequence variations, allowing for rapid data analysis with reduced resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all genetic data is stored, then complete genetic information is preserved, but significant duplications and overlap occur resulting in increased storage requirements and processing complexity
Solution Approach 1:
The patent extracts only the variant information from the complete genetic data and stores it in a separate variants table, while the reference genome is stored in a reference_genome table. This extraction principle eliminates duplications by storing only the differences (variants) rather than the entire genetic dataset multiple times, thereby preserving complete genetic information through the combination of reference genome and variants while significantly reducing storage requirements.
2Reliability
If VCF text files are analyzed directly, then data integrity is maintained, but analysis is lengthy and consumes significant process resources
Solution Approach 1:
The patent performs preliminary action by pre-processing the VCF text file and transforming it into a structured relational database schema before analysis. The ETL process pre-organizes the data into normalized tables with defined relationships, creating an optimized structure that enables faster querying and analysis while maintaining data integrity through structured storage and indexing.
Solution Approach 2:
The patent substitutes the mechanical text file analysis system with a database-based system. Instead of processing raw text files during analysis, the data is transformed into a relational database with optimized data structures, indexes, and query capabilities, replacing the inefficient text parsing mechanism with a high-performance database engine that significantly improves analysis speed and resource efficiency.
3Productivity
If a rigid database schema is used, then data structure is simple and queries are fast, but it cannot accommodate evolving and extensible data formats like VCF
Solution Approach 1:
The patent segments the database into multiple normalized tables (reference_genome, variants, samples, etc.) with defined relationships. This segmentation allows the schema to accommodate evolving data formats by adding new tables or columns as needed without affecting the entire database structure. Each segment can be independently modified to support new data types or formats while maintaining query performance on existing data through optimized table structures and indexes.
Solution Approach 2:
The patent implements a dynamic database schema that can adapt to evolving data formats. The normalized relational structure allows for flexible addition of new columns, tables, and relationships to accommodate new VCF formats or data types. The schema evolution capability enables the database to grow and adapt alongside evolving genomic data standards while maintaining the performance benefits of structured storage through proper indexing and query optimization.
4Productivity
If metadata and content are stored in separate tables, then data organization is improved and queries are more efficient, but the mapping complexity increases
Solution Approach 1:
The patent implements a universal mapping framework that handles both metadata and content data through a consistent ETL process. The same transformation logic and database structure accommodate different data types (metadata, variants, samples, references) uniformly. This universal approach simplifies the mapping process by applying the same principles across all data categories, reducing overall complexity despite the multi-table structure, while maintaining query efficiency through proper normalization and indexing.
Data Source
AI summary
Data including a text file is received. The text file is arranged in an extensible format and includes a plurality of metadata lines, a header line, and a plurality of content lines. Metadata from the metadata lines is mapped to a plurality of metadata tables in a database that are formed according to a relational database schema using prefix parameters from each metadata line. Content from the content lines is mapped to a plurality of content tables in the database that are formed according to the relational database schema using the header line. A first subset of the content tables have a static structure and a second subset of the content tables have a dynamic structure. Related apparatus, systems, techniques and articles are also described.