Nucleotide Sequence Compression Using Consensus Template Comparison
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Solution Overview
Problem
The vast amount of DNA sequencing data generated by modern sequencing techniques poses a significant challenge in storage, computation, and communication due to its enormous size, necessitating an efficient data compression method to manage and process this information effectively.
Innovation Solution
A data compression method that involves obtaining multiple reading sequences from the same source, comparing them using seed tables with specific lengths and seeding start sites, generating a final template sequence, and compressing the sequences based on their differences to produce a compression file, utilizing a processor and receiving unit within a sequence compression device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If raw read sequences are generated and stored in large amounts to achieve higher DNA sequencing accuracy, then sequencing accuracy is improved, but storage burden and data management complexity increase significantly
Solution Approach 1:
Multiple reading sequences from the same DNA source are merged into a single consensus sequence by identifying and comparing common regions. The patent combines information from multiple sequences (first reading sequence, second reading sequence, etc.) to generate one consolidated consensus sequence, reducing storage requirements while preserving sequencing accuracy through the collaborative information from all input sequences.
Solution Approach 2:
Instead of storing multiple complete reading sequences, the patent creates a simplified consensus sequence that copies only the essential information from the original sequences. The consensus sequence serves as a representative copy that captures the reliable data from multiple readings, eliminating redundant storage of identical or highly similar sequence information.
2Reliability
If multiple reading sequences are processed and compared to generate consensus sequences, then data quality and reliability are improved, but computation time and processing complexity increase
Solution Approach 1:
The patent divides the comparison process into segments by identifying common regions between sequences. Instead of comparing entire sequences from start to finish, the method segments the comparison to focus on regions that are common to multiple reading sequences, thereby reducing the total computation time while still achieving reliable consensus sequence generation.
Solution Approach 2:
The patent performs preliminary identification of common regions between reading sequences before conducting detailed comparisons. By pre-identifying which regions are common across multiple sequences, the method prepares the data in advance to facilitate faster and more efficient consensus sequence generation, reducing overall processing time.
3Measurement precision
If comprehensive comparison of reading sequences is performed to ensure data accuracy, then measurement precision is improved, but device complexity and processing requirements increase
Solution Approach 1:
The patent applies different processing strategies to different regions of the sequences based on their characteristics. Common regions are processed through identification and consolidation, while variable regions are handled differently. This local quality approach ensures high measurement precision in regions that require it while simplifying processing in regions where full complexity is unnecessary.
Data Source
AI summary
The invention provides a data compression method, comprising: (a) obtaining a first reading sequence and a second reading sequence from an identical source by a receiving unit; (b) comparing the first reading sequence with the second reading sequence according to a comparison condition to generate a sequence comparison result by the processor; (c) outputting a final template sequence according to the sequence comparison result by the processor; (d) comparing the final template sequence to each of the first and second reading sequences, to generate a respective difference between the final template sequence and each of the first and second reading sequences by the processor; and (e) compressing the first and second reading sequences according to the final template sequences and all generated differences between the final template sequence and the first and second reading sequences, to generate a compression file by the processor.