Consensus Compression of Molecular-Tagged Reads for Variant Calling
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Solution Overview
Problem
Current methods for storing and processing large amounts of molecular tagged nucleic acid sequence data are inefficient in terms of memory usage and computational efficiency, particularly for variant calling operations, without compromising data quality.
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, along with the number of members in each family, to reduce data size while maintaining variant calling accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If molecular tagged nucleic acid sequence data is stored and processed using conventional methods, then complete data quality is maintained for variant calling, but memory requirements are excessive and computational efficiency is poor
Solution Approach 1:
The patent segments the large set of sequence reads into smaller families based on molecular tags. Each family shares common characteristics (molecular tag, consensus sequence), allowing the data to be processed in manageable units rather than as one large dataset, thus reducing memory requirements while preserving all necessary information for accurate variant calling
Solution Approach 2:
The patent transforms the data representation by calculating consensus sequences and consensus flow space signal measurements for each family. This parameter transformation condenses multiple similar reads into a single representative consensus with aggregated signal information, reducing data volume while maintaining the statistical power needed for reliable variant detection
2Reliability
If molecular tagged nucleic acid sequence data is stored and processed using conventional methods, then complete data quality is maintained for variant calling, but computational efficiency is poor
Solution Approach 1:
By dividing the computational task into family-level operations rather than read-level operations, the patent reduces the complexity of variant calling. Each family is processed independently with pre-calculated consensus sequences, eliminating redundant computations across similar reads and significantly improving computational efficiency
Solution Approach 2:
The patent performs preliminary calculations of consensus sequences and consensus flow space signal measurements before variant calling. This pre-processing step consolidates information from multiple reads into compact family-level representations, so that subsequent variant calling operations work with simplified data structures, reducing overall computational burden
3Quantity of substance
If sequence reads are grouped by molecular tag to form families and consensus values are calculated, then data size is reduced for efficient storage and processing, but data complexity increases
Solution Approach 1:
The patent creates a universal data structure (consensus family representation) that serves multiple functions: it reduces data size, enables efficient storage, and maintains all necessary information for variant calling. The consensus sequence, consensus flow space signal measurements, and family member count are multi-functional elements that compress data while preserving analytical capability
Solution Approach 2:
The patent creates simplified copies (consensus sequences) that represent groups of similar reads. These consensus copies capture the essential information from multiple reads in a compact form, allowing the system to work with reduced data complexity while maintaining the ability to detect variants through the aggregated signal measurements
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.