Consensus Compression of Molecular-Tagged Reads for Variant Calling
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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, thereby reducing data size 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 become excessively large and computational efficiency deteriorates
Solution Approach 1:
Multiple sequence reads originating from the same polynucleotide molecule (identified by matching molecular tags) are merged into a single consensus sequence. This combining process reduces the total quantity of data while preserving the essential genetic information needed for accurate variant calling, as the consensus sequence represents the collective information from all reads in the family.
Solution Approach 2:
Instead of storing and processing every individual sequence read, the invention creates a representative copy (consensus sequence) that captures the essential information from multiple reads. This consensus copy is then used for downstream analysis, significantly reducing memory requirements while maintaining variant calling accuracy.
2Measurement precision
If all sequence reads are processed individually for variant calling, then comprehensive analysis is performed, but computational time and resource consumption increase
Solution Approach 1:
Sequence reads with identical molecular tags are merged into consensus sequences before variant calling. This merging reduces the number of sequences that need to be individually processed, thereby decreasing computational time and resource consumption while maintaining detection accuracy through the use of consensus information.
Solution Approach 2:
The consensus sequence is calculated in advance before the variant calling step. This preliminary action of creating consensus sequences prepares the data in a compressed, information-rich format that accelerates subsequent computational analysis without sacrificing the precision needed for accurate variant detection.
3Quantity of substance
If consensus sequences are calculated from multiple reads, then data compression is achieved, but computational overhead for consensus calculation is introduced
Solution Approach 1:
The processing is segmented into distinct phases: first grouping reads by molecular tag, then calculating consensus sequences for each group, and finally performing variant calling on the compressed consensus data. This segmentation allows the system to manage complexity by handling one task at a time rather than processing all operations simultaneously on the full dataset.
Solution Approach 2:
The consensus sequence calculation automatically utilizes the molecular tag information that is already present in the sequence reads. The system self-organizes the reads into families based on their tags and performs consensus calculation within each family, reducing the need for external coordination or complex preprocessing steps.
Data Source
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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.