Gene Mutation Dictionary Compression for Genome Storage
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Solution Overview
Problem
Current methods for genomic data storage are inadequate due to the large size of genomic data, with existing data compression techniques only offering limited reduction in storage space and requiring significant resources for decompression, making it challenging to store and manage vast amounts of genomic information efficiently.
Innovation Solution
A method and device for creating a gene mutation dictionary that compresses genomic data by partitioning the genome into biologically significant unit regions, statistically analyzing mutation patterns, and assigning unique index numbers, allowing for significant data reduction by storing only reference numbers of mutant patterns instead of actual mutations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data compression tools such as gzip are used to reduce genomic data storage space, then storage space is reduced to some extent, but the compression rate is only about 60% and the compressed data still occupies a lot of storage space
Solution Approach 1:
The genome is divided into multiple unit regions of biological significance (such as genes, exons, introns, regulatory regions). Each unit region is independently analyzed and encoded, allowing for more efficient compression by capturing mutation patterns at the regional level rather than processing the entire genome as a single block.
Solution Approach 2:
The invention transforms genomic data from raw sequence format to a compressed representation using mutant patterns and index numbers. By changing the data representation parameters (from storing actual mutation details to storing reference indices), the compression rate dramatically increases to approximately 25,000 times while maintaining data integrity.
2Ease of operation
If genomic data is repeatedly compressed and decompressed for individuals to view their genetic information, then data can be accessed, but the process is troublesome and requires large storage to support decompression
Solution Approach 1:
The invention creates a reference genome that serves as a compressed copy containing all necessary mutation pattern information. Individual genomic data is represented by referencing this compressed copy with minimal additional information (index numbers), eliminating the need for full decompression while maintaining data accessibility.
Solution Approach 2:
The genome is pre-partitioned into unit regions and mutant patterns are pre-established before individual data processing. This preliminary organization allows for efficient querying and access of individual genetic information without requiring complex decompression operations, as the structural framework is already in place.
3Measurement precision
If the entire genome is processed as a single unit, then comprehensive analysis is possible, but the data size remains large and compression efficiency is low
Solution Approach 1:
The genome is segmented into biologically meaningful unit regions (genes, exons, introns, regulatory elements). Each segment is independently analyzed for mutation patterns, enabling comprehensive genomic analysis while significantly reducing data size through localized pattern recognition and indexing.
Solution Approach 2:
The invention adds a hierarchical dimension to genome analysis by organizing data from the nucleotide level to the unit region level. This dimensional transformation allows comprehensive analysis at multiple levels while compression occurs at the regional level, achieving both analytical depth and data efficiency.
Data Source
AI summary
Provided are a method and device for creating a gene mutation dictionary, and a method and device for compressing genomic data using the gene mutation dictionary. The method for creating a gene mutation dictionary includes: obtaining genome sequence data of a plurality of individuals of a species and reference genome data of the species; aligning genome sequence data of each individual to the reference genome data to obtain a mutation result of the genome sequence data of each individual relative to the reference genome data; partitioning a genome of the species into a plurality of unit regions of biological significance; and generating a plurality of mutant patterns of the individuals in each unit region by statistically analyzing mutant status for each unit region based on the mutation result, and numbering the mutant patterns, to obtain the gene mutation dictionary.