Genome Data Compression Using K-Mer Prediction Models
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Solution Overview
Problem
Current gene sequencing data compression methods, particularly reference-based algorithms, face challenges in achieving a balance between compression ratio and compression/decompression time, with instability in performance due to the selection of reference genomes, and require improved preprocessing methods for enhanced efficiency.
Innovation Solution
A gene sequencing data compression preprocessing method that involves generating a prediction data model by mapping short string K-mers from reference genome data to predict adjacent characters, allowing for accurate prediction and reversible computing to achieve lower compression ratios and faster processing times without precise comparison of gene sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If reference-based compression algorithms are used to achieve better compression ratios, then compression ratio is improved, but compression/decompression time increases and performance becomes unstable due to reference genome selection
Solution Approach 1:
The patent applies preliminary action by pre-processing the reference genome data to build a prediction model before actual compression. The prediction model is trained in advance on the reference genome characteristics, allowing the compression algorithm to make faster predictions during the actual compression process without performing complex real-time analyses, thus reducing compression/decompression time while maintaining good compression ratios.
Solution Approach 2:
The patent changes parameters by adapting the compression strategy based on the specific characteristics of the reference genome. Instead of using a fixed compression approach, the system adjusts compression parameters dynamically according to the reference genome's properties, which stabilizes performance across different reference genomes while maintaining efficient compression ratios and speeds.
2Quantity of substance
If reference-based compression algorithms are used to achieve better compression ratios, then compression ratio is improved, but performance becomes unstable due to reference genome selection
Solution Approach 1:
The patent addresses performance stability by dynamically adjusting compression parameters based on the specific characteristics of each reference genome. The system analyzes reference genome properties and adapts compression settings accordingly, which eliminates the instability caused by using different reference genomes while maintaining high compression ratios.
Solution Approach 2:
The patent implements feedback mechanisms by evaluating the effectiveness of compression operations and adjusting the prediction model based on performance metrics. The system continuously refines its compression strategy by feeding back performance information, ensuring stable and reliable compression ratios across different reference genome selections.
3Measurement precision
If precise comparison of gene sequences is performed to achieve accurate compression, then compression accuracy is improved, but computing efficiency decreases
Solution Approach 1:
The patent extracts only the essential features and characteristics from the reference genome data that are necessary for compression, rather than performing complete and precise comparisons of entire gene sequences. By extracting key predictive features, the system achieves sufficient compression accuracy while dramatically reducing the computational burden and improving processing efficiency.
Solution Approach 2:
The patent applies partial action by performing only the necessary comparison operations required for effective compression, rather than exhaustive precise comparisons. The prediction model performs selective analysis on critical sequence features, achieving adequate compression accuracy with significantly reduced computing efficiency requirements.
Data Source
AI summary
The present invention discloses a gene sequencing data compression preprocessing, compression and decompression method, a system, and a computer-readable medium. The preprocessing method implementation steps include: obtaining reference genome data; obtaining a mapping relationship between a short string K-mer and a prediction character c to obtain a prediction data model P1 containing any short string K-mer in the positive strand and negative strand of a reference genome and the prediction character c in a corresponding adjacent bit. The compression and decompression methods relate to performing compression/decompression on the basis of the prediction data model P1. The system is a computer system including a program for executing the previous method. The computer-readable medium includes a computer program for executing the previous method. The present invention can be oriented towards lossless gene sequencing data compression, provides fully effective information for a high-performance lossless compression and decompression algorithm for gene sequencing data.