Consensus Compression of Molecular-Tagged Reads for Variant Calling
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Solution Overview
Problem
There is a need for new methods to compress molecular tagged nucleic acid sequence data to reduce memory requirements for storage and improve computational efficiency for variant calling operations, while maintaining the quality of variant calling.
Innovation Solution
A method for compressing molecular tagged nucleic acid sequence data involves receiving sequence reads, flow space signal measurements, and sequence alignments associated with molecular tags, grouping reads by tags, calculating consensus flow space signal measurements and standard deviations, determining consensus base sequences and alignments, and generating a compressed data structure that includes consensus data for each family of sequence reads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If molecular tagged nucleic acid sequence data is stored in full detail, then data quality for variant calling is maintained, but memory requirements and storage costs increase
Solution Approach 1:
Multiple sequence reads from the same original polynucleotide molecule (identified by matching molecular tags) are merged into a single consensus sequence. This consolidation reduces data volume while preserving the essential genetic information needed for accurate variant calling, as the consensus sequence represents the aggregated signal from multiple measurements of the same underlying molecule.
Solution Approach 2:
Consensus sequences are generated in advance during data processing, before variant calling is performed. This preliminary consolidation of data reduces the amount of information that needs to be stored and processed during subsequent variant calling operations, while the consensus generation process preserves critical quality information through statistical measures like standard deviation calculations.
2Measurement precision
If all sequence reads are processed individually, then processing accuracy is maintained, but computational efficiency decreases
Solution Approach 1:
Sequence reads that originate from the same polynucleotide molecule (grouped by molecular tags) are processed together to generate a single consensus sequence, rather than processing each read independently. This merging reduces the total number of processing operations required while maintaining accuracy through statistical consensus methods that account for sequencing errors and variations.
Solution Approach 2:
Instead of processing multiple identical or near-identical reads separately, a single consensus sequence is created that represents the collective information. This consensus copy can then be used for variant calling, reducing computational load while preserving the essential genetic information through the consensus generation process that incorporates quality metrics.
3Quantity of substance
If consensus sequences are generated for all families, then data compression is achieved, but processing time for consensus generation increases
Solution Approach 1:
The data processing is segmented into distinct phases: first grouping reads by molecular tags into families, then generating consensus sequences for each family. This segmentation allows for efficient organization of data structures and enables parallel processing of different families, reducing overall processing time while achieving comprehensive data compression across all sequence reads.
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.