Genomic File Format with Master Index Table
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current genomic data storage solutions are inefficient due to the use of multiple file formats at different stages of the genomic information life cycle, leading to linear growth in storage space, high computational costs, and limitations in selective access, encryption, and data transfer efficiency, hindering timely genomic analysis and disease response.
Innovation Solution
A Genomic File Format that classifies sequence reads into layers of homogeneous metadata, allowing selective access and efficient compression, with a Master Index Table and Access Units enabling incremental updates and efficient data retrieval, and supporting encryption of specific data subsets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple file formats are used at different stages of genomic data processing, then data can be stored according to specific processing requirements, but storage space grows linearly and data management becomes complex
Solution Approach 1:
The patent implements a universal Genomic File Format that can store heterogeneous genomic data (raw sequences, aligned reads, variants, annotations) in a single standardized structure. This multi-functional format eliminates the need for multiple specialized file formats while maintaining adaptability to different processing stages through its flexible data model and indexing mechanisms.
Solution Approach 2:
The patent merges previously separate file formats and data structures into a single integrated Genomic File Format. By combining raw sequence data, alignment information, variant calls, and annotations into one unified structure with centralized indexing, the system reduces data management complexity while preserving all necessary functionality for different genomic analysis stages.
2Reliability
If entire files are transferred for genomic data analysis, then complete data is available for processing, but transfer time and bandwidth consumption increase significantly
Solution Approach 1:
The patent extracts and indexes key metadata (mapping positions, read identifiers, variant positions) into a Master Index Table that can be queried independently. This allows users to extract and transfer only the specific genomic data subsets needed for particular analyses, rather than transferring entire large-scale genomic datasets, thereby reducing transfer time and bandwidth consumption while maintaining data completeness for the required analysis scope.
Solution Approach 2:
The patent segments genomic data into logically organized components (raw sequences, aligned reads, variants, annotations) with a hierarchical structure. This segmentation enables selective access and transfer of specific data segments based on analysis requirements, allowing complete necessary data to be transferred efficiently without including unnecessary portions of the entire dataset.
3Productivity
If selective access to specific data subsets is implemented, then data retrieval efficiency improves, but data structure complexity increases
Solution Approach 1:
The patent performs preliminary organization of genomic data into a hierarchical structure with a Master Index Table that pre-categorizes and indexes all data by type, position, and characteristics. This preliminary structuring enables efficient selective access during retrieval operations without requiring complex query logic, as the data is already organized for optimal access patterns. The indexing structure anticipates common access scenarios and optimizes for them in advance.
4Ease of operation
If comprehensive indexing of all genomic data is created, then selective access capability is enhanced, but storage overhead and processing time increase
Solution Approach 1:
The patent implements a hierarchical indexing system where the Master Index Table provides global overview and coordination, while individual data sections maintain their own local indexes optimized for specific access patterns. This local quality approach allows selective access to be achieved efficiently for each data type without requiring a single comprehensive index to process all queries, thereby reducing overall indexing processing time while maintaining enhanced selective access capability.
Data Source
Figure 1~2
Figure 3~4
Figure 5~7
AI summary
Method and apparatus for the representation of genome sequence data organized in a structured file format. The data structure contains the representation of nucleotide sequences: in compressed form, aligned and referenced to one or more reference sequences and classified according to different degrees of matching accuracy. The classified and aligned reads are coded in the form of layers of syntax elements, which including a header information are partitioned into independent or dependent access units.