Genomic Data Compression Using Intermediary Data Segmentation
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Solution Overview
Problem
Conventional genomic data compression techniques are inefficient in handling intermediary data such as secondary characters and header information, leading to inaccurate compression and high processing costs, which hinders quick storage, retrieval, and transmission of genomic data.
Innovation Solution
A method that identifies and separates intermediary data from primary characters in genomic sequences, generating at most two most-frequent character files and a least-frequent characters file, which are then compressed using unary and binary encoding respectively, to produce lossless and efficient compressed genomic data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Volume of stationary object
If conventional compression techniques are used on genomic data, then storage space is reduced, but processing time increases and data accuracy deteriorates due to inability to handle intermediary data
Solution Approach 1:
The patent segments genomic data into two distinct parts: primary characters (A, T, G, C) and intermediary data (header information, secondary characters). This segmentation allows each part to be processed differently - primary characters undergo frequency analysis and unary encoding while intermediary data is preserved separately, thereby reducing overall storage requirements without increasing processing time for critical genomic information.
Solution Approach 2:
The patent extracts intermediary data (headers and secondary characters) from the main genomic sequence data. By removing these non-essential components from the compression process, the system achieves faster processing of the core genomic information while the extracted intermediary data can be stored separately or compressed using different methods, thus resolving the contradiction between storage reduction and processing speed.
2Volume of stationary object
If conventional compression techniques are used on genomic data, then storage space is reduced, but compression accuracy deteriorates due to inaccurate handling of intermediary data
Solution Approach 1:
The patent segments genomic data into primary characters and intermediary data, applying different processing strategies to each. Primary characters are compressed using frequency-based unary encoding which maintains high accuracy, while intermediary data is handled separately to preserve its integrity. This segmented approach eliminates the accuracy loss that occurs when conventional techniques treat all data uniformly.
Solution Approach 2:
The patent applies local quality by using different compression methods for different parts of the genomic data. Frequency analysis and unary encoding are applied specifically to primary characters where high compression efficiency is needed, while intermediary data receives different treatment to maintain its structural integrity and accuracy, thus resolving the contradiction between storage reduction and compression accuracy.
3Device complexity
If genomic data is compressed without separating intermediary data, then processing is simpler, but compression efficiency deteriorates and processing costs increase
Solution Approach 1:
The patent segments genomic data into primary characters and intermediary data, which initially appears to increase complexity. However, this segmentation enables much higher compression efficiency for the primary characters through frequency analysis and unary encoding. The overall system complexity remains manageable because the segmentation follows clear rules and the separate processing of each segment is computationally efficient, thus resolving the contradiction between processing simplicity and compression efficiency.
Data Source
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AI summary
The present subject matter discloses a system and a method for compression of genomic data. In one embodiment, the method for compression of genomic data includes obtaining modified genomic data from genomic data based at least in part on intermediary data identified from the genomic data. In one implementation, the modified genomic data includes a plurality of primary characters. The genomic data may then be modified to generate one or more most-frequent character files based at least on a most-frequent character and a second most-frequent character from among the plurality of primary characters. Further, based at least on the one or more most-frequent character files and the modified genomic data, a least-frequent characters file may be created from the modified genomic data.