Consensus Compression of Molecular-Tagged Sequence Reads
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Solution Overview
Problem
Current methods for processing molecular tagged nucleic acid sequence data are inefficient in terms of memory usage and computational efficiency, particularly for variant calling operations, due to the large amounts of data generated from nucleic acid sequencing techniques.
Innovation Solution
A method involving the grouping of sequence reads with the same molecular tag to calculate consensus flow space signal measurements and standard deviations, determining a consensus base sequence and alignment, and generating a compressed data structure that includes these consensus values, allowing for reduced data storage and processing requirements without compromising variant calling quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If molecular tagged nucleic acid sequence data is stored and processed using conventional methods, then complete sequence information is preserved for analysis, but memory requirements and computational resources increase significantly
Solution Approach 1:
The patent groups multiple sequence reads that share the same molecular tag into a single consensus sequence. This merging process combines redundant information from multiple reads representing the same original polynucleotide molecule, thereby reducing the total data volume while preserving the essential genetic information needed for accurate variant calling.
Solution Approach 2:
Instead of storing and processing every individual sequence read, the patent creates a compressed representation (consensus sequence) that serves as a simplified copy containing the essential information. This consensus sequence is then used for downstream variant calling operations, significantly reducing memory requirements while maintaining analytical accuracy.
2Reliability
If all sequence reads are processed individually for variant calling, then comprehensive analysis is performed, but computational efficiency decreases
Solution Approach 1:
The patent merges multiple individual sequence processing operations into a single consensus sequence processing operation. By combining the information from multiple reads that share the same molecular tag before performing variant calling, the system reduces the number of computational operations required while maintaining the sensitivity and accuracy of variant detection.
Solution Approach 2:
The patent performs preliminary grouping and consensus sequence generation before the actual variant calling process. This preliminary action of organizing reads into families and creating consensus sequences simplifies the subsequent variant calling operation, making it more computationally efficient while ensuring that all necessary information is preserved for accurate analysis.
3Quantity of substance
If molecular tag grouping and consensus calculation is performed, then data compression is achieved, but processing complexity increases
Solution Approach 1:
The patent segments the sequence reads into distinct families based on their molecular tags. This segmentation organizes the data into manageable groups that can be processed independently, making the overall complex task of data compression more tractable by breaking it down into smaller, systematic steps of grouping and consensus calculation.
Data Source
AI summary
A method for compressing molecular tagged sequence data includes: grouping sequence reads associated with a molecular tag sequence to form a family of sequence reads, corresponding vectors of flow space signal measurements and corresponding sequence alignments, calculating an arithmetic mean of the corresponding vectors of flow space signal measurements to form a vector of consensus flow space signal measurements, calculating a standard deviation of the corresponding vectors of flow space signal measurements to form a vector of standard deviations, determining a consensus base sequence based on the vector of consensus flow space signal measurements, determining a consensus sequence alignment and generating a compressed data structure comprising consensus compressed data, the consensus compressed data including for each family, the consensus base sequence, the consensus sequence alignment, the vector of consensus flow space signal measurements, the vector of standard deviations and the number of members.