Genomic Quality Value Compression via Alignment Contexts
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for compressing quality values in genomic sequencing data are inefficient and do not effectively utilize alignment information, leading to large file sizes and suboptimal compression results.
Innovation Solution
Utilize novel contexts derived from alignment information, including match/mismatch with reference bases and error analysis, to enhance compression using count-based adaptive arithmetic coding and neural-network prediction-based arithmetic coding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If existing compression methods (QVZ, CALQ) are used for quality values, then compression is performed on separate quality sequences, but compression efficiency is limited and file sizes remain large
Solution Approach 1:
The patent merges quality value compression with alignment information compression into a unified framework. Instead of compressing quality values separately as in QVZ and CALQ, the invention integrates quality values with alignment contexts (matches, mismatches, indels) to create joint probability models that capture dependencies between alignment status and quality scores, resulting in significantly smaller file sizes and improved compression efficiency
Solution Approach 2:
The invention adds the dimension of alignment information to the compression process. By incorporating alignment contexts (match/mismatch/indel status) as additional dimensions beyond just quality value sequences, the patent creates a multi-dimensional compression model that leverages correlations between alignment status and quality scores, achieving superior compression ratios
2Reliability
If alignment information is not utilized in compression, then compression process is simpler, but compression results are suboptimal and quality value preservation is poor
Solution Approach 1:
The patent introduces alignment information as an intermediary that mediates between the quality values and the compression process. Alignment contexts serve as intermediate variables that capture relationships between sequencing quality and alignment status, enabling the compressor to model joint probability distributions that improve quality value preservation while managing complexity through structured context modeling
3Productivity
If multiple contexts are used for compression, then compression efficiency improves, but computational overhead increases
Solution Approach 1:
The patent segments the compression process into distinct context types (match contexts, mismatch contexts, indel contexts) and processes them independently through separate arithmetic coding passes. This segmentation allows the system to manage computational complexity by handling each context category with dedicated probability models, improving overall compression efficiency while controlling resource consumption through organized processing
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A method for compressing information includes accessing a read of genomic sequencing data, aligning the read to a reference, generating alignment data based on alignment of the read, obtaining a set of contexts based on the alignment data, and compressing quality values corresponding to the alignment data based on the set of contexts. The alignment data may provide an indication of errors in the genomic sequencing data, and each of the quality values may provide an indication of a probability of error at one or more bases in the genomic sequencing data.