Molecular Tag Sequence Compression for Efficient Variant Calling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for processing large volumes of molecular tagged nucleic acid sequence data are inefficient in terms of memory usage and computational resources, necessitating improved compression techniques without compromising the quality of variant calling operations.
Innovation Solution
A method involving grouping sequence reads with the same molecular tag, calculating consensus flow space signal measurements and alignments, determining a consensus base sequence, and generating a compressed data structure that includes consensus base sequence, alignment, and standard deviations for each family of reads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If molecular tagged nucleic acid sequence data is stored and processed using conventional methods, then complete data is available for analysis, but memory requirements and computational resources are excessively high
Solution Approach 1:
The patent segments the large volume of molecular tagged nucleic acid sequence data by grouping reads with identical molecular tags into families. For each family, it calculates consensus values (mean flow space signal measurements, consensus base sequence, consensus alignment) that represent the entire family. This segmentation reduces the data volume from individual reads to family-level consensus data, significantly decreasing memory requirements while preserving the essential information needed for accurate variant calling.
2Productivity
If all sequence reads are processed individually for variant calling, then comprehensive analysis is performed, but computational efficiency is low
Solution Approach 1:
The patent merges multiple sequence reads belonging to the same molecular tag family into a single consensus representation. By combining the flow space signal measurements and calculating mean values, consensus base sequences, and consensus alignments, the method processes one consensus data structure per family instead of multiple individual reads. This merging dramatically improves computational efficiency for variant calling while maintaining measurement precision through the statistical consolidation of signals.
3Loss of information
If consensus data is calculated for compressed families, then data volume is reduced, but information loss may occur
Solution Approach 1:
The patent creates consensus copies that represent families of sequence reads. Instead of storing all original reads, it generates compressed consensus data structures containing mean flow space signal measurements, consensus base sequences, and consensus alignments. These consensus copies preserve the essential informational content needed for variant calling by statistically representing the collective signal of multiple reads, achieving high compression ratios while maintaining the accuracy required for reliable variant detection.
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.