In-Memory Database for Real-Time Genomic Data Analysis
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Solution Overview
Problem
Current genome data processing pipelines face challenges in efficiently integrating additional tools and processing steps, leading to increased processing time, and struggle with consolidating annotation results from distributed databases, which affects the quality and speed of genomic data analysis.
Innovation Solution
A computer-based system utilizing an in-memory database with a platform layer and worker framework for parallel processing of nucleotide sequence data, combined with an updater framework for automatic annotation updates, enables fast and high-quality genomic data processing by storing intermediate and final results in-memory, reducing media breaks and enhancing throughput.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If additional processing tools and steps are added to the genome data processing pipeline to improve result quality, then the quality of the result is improved, but the processing time significantly increases
Solution Approach 1:
The patent merges multiple annotation databases (UCSC, ENSEMBL, NCBI) into a single integrated in-memory database system. This consolidation allows the processing pipeline to access all annotation data through one unified interface rather than querying multiple separate databases, thereby maintaining high result quality while reducing the time penalty associated with multiple sequential access operations.
Solution Approach 2:
The system performs preliminary actions by pre-loading all annotation data from multiple databases into the in-memory database before the actual genome data processing begins. This advance preparation ensures that when the processing pipeline needs annotation information, all data is already available in memory, eliminating the time-consuming step of accessing external databases during processing while still providing comprehensive annotation coverage for high result quality.
2Loss of information
If annotation data from multiple distributed databases is accessed separately to improve result quality, then the completeness of annotations is improved, but the processing time negatively increases
Solution Approach 1:
The patent combines data from multiple distributed annotation databases (UCSC Genome Browser, ENSEMBL, NCBI) into a single integrated in-memory database. This merging preserves the completeness of annotations from all sources while eliminating the time penalty of accessing multiple separate databases, as all annotation data becomes available through one unified in-memory interface during genome data processing.
Solution Approach 2:
The in-memory database acts as an intermediary between the genome data processing pipeline and the external annotation databases. Instead of the pipeline directly querying multiple external databases (which is time-consuming), the in-memory database serves as a local cache that holds all annotation data, providing fast access while maintaining annotation completeness from all external sources.
3Reliability
If conventional disk-based database systems are used for storing intermediate results to ensure data persistence, then data reliability is improved, but processing speed decreases due to media breaks
Solution Approach 1:
The patent changes the storage medium parameter from disk-based to in-memory storage for the database system. By storing all intermediate and final results in the in-memory database rather than on disk, the system eliminates media breaks and I/O bottlenecks, achieving significant processing speed improvements while maintaining data reliability through the database's transactional integrity and persistence mechanisms.
Data Source
AI summary
A system and a method for analyzing and evaluating genome data in the course of personalized medicine. In-memory technology is provided for processing of genome data and its real-time analysis as a holistic process in the course of personalized medicine. The cloud application helps physicians and researchers to identify the genetic roots for certain tumor types in the treatment of diseases correlating to genomic variants or mutations, such as cancer diseases. The system combines the latest international research results with patient-specific genomic data while eliminating the need for long-lasting manual searches of all dispositions in distributed international research and literature data sources.