GPU Genome Sequence Compression via Character Difference Extraction
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Solution Overview
Problem
Current methods for compressing and querying genome sequences are inefficient, particularly when dealing with large datasets, as they do not effectively utilize parallel processing capabilities to filter and analyze data quickly and accurately.
Innovation Solution
A method utilizing GPU processing units to identify and align genome reader strings with a reference genome, filter out similar characters, and store data in chunks using clustering techniques, enabling efficient compression and querying through non-mutating operators like join, filter, and sort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional CPU-based methods are used for compressing and querying genome sequences, then implementation simplicity is maintained, but processing speed and efficiency deteriorate due to inability to handle large datasets effectively
Solution Approach 1:
The patent segments the genome sequence data into discrete characters and positions, allowing parallel processing of multiple characters simultaneously across different GPU cores. Each GPU thread handles a specific character position across multiple readers, enabling massive parallelization of the compression and querying operations.
Solution Approach 2:
The patent replaces traditional CPU-based sequential processing with GPU-based parallel processing architecture. This substitution leverages the massively parallel nature of GPU computing to simultaneously process multiple genome readers, achieving significant speedup in compression and querying operations.
2Productivity
If all genome reader characters are stored and processed, then complete data accuracy is maintained, but data volume and processing time increase significantly
Solution Approach 1:
The patent extracts only the essential information needed for genome querying by comparing each reader character against the reference genome and storing only the differences (mutations). This extraction approach dramatically reduces data volume while preserving all necessary genetic information for analysis.
Solution Approach 2:
The patent applies different processing treatments to different characters based on their significance. Characters matching the reference genome are filtered out, while differing characters (mutations) are retained and stored with their position information. This local quality approach optimizes storage by keeping only the variable information.
3Measurement precision
If complex filtering and analysis operations are performed on uncompressed genome data, then analysis accuracy is maintained, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary compression and filtering of genome readers before querying operations. By pre-processing the data to extract only the essential variant information and organize it in a compressed format, subsequent querying, filtering, and analysis operations can be performed much faster while maintaining full accuracy.
Data Source
AI summary
The present invention provides a method for compressing genome sequences readers using GPU processing unit. The method comprising the steps of: identifying position of each given genome reader characters string in the sequence of a reference genome, determining alignment of each reader string within the reference genome, comparing each reader characters string to corresponding reference genome sequence based on determined alignment, filtering characters in each reader by GPU processor by eliminating similar characters and extracting only characters differences in association to their position in the genome sequence and recording filtered data of each reader in association to its alignment in genome reference at the genome compressed database.