DNA Sequence Compression via Mismatch Classification and Histogram Coding
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Solution Overview
Problem
Current compression algorithms, such as GZIP and CRAM, offer limited lossless compression ratios for large volumes of DNA sequence data, which are increasingly generated in genomics, necessitating more efficient storage and transfer methods.
Innovation Solution
A processor-implemented method aligns a reference genome with DNA sequences, classifies and sorts read sequences based on mismatched bases, builds a histogram, and codes the sequences using techniques like run-length coding and LZMA to achieve improved compression, specifically targeting the SAM file format for efficient DNA sequence compression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If general purpose compression algorithms (GZIP, BZIP2, 7ZIP) are used to compress DNA sequences, then the compression process is simple and fast, but the compression ratio is only around 3:1 which is insufficient for large volumes of genomic data
Solution Approach 1:
The patent segments the DNA sequence data into individual read sequences and further divides them into groups based on the number of mismatches with the reference genome. This segmentation allows different compression strategies to be applied to different groups, achieving high compression ratios while managing complexity through systematic organization of the data processing workflow
Solution Approach 2:
The patent performs preliminary alignment of DNA read sequences against a reference genome before compression, classifying sequences by their mismatch count. This preliminary action creates an organized structure that enables more efficient compression in subsequent steps, transforming raw data into a format optimized for compression without requiring complex real-time compression algorithms
2Productivity
If CRAM toolkit is used for reference-based compression of DNA sequences, then the compression is reference-based which is suitable for genomic data, but the toolkit is slow and the lossless compression ratio is not impressive
Solution Approach 1:
The patent divides the compression task into distinct segments: alignment phase, classification phase, and compression phase. By separating these operations, the system can optimize each phase independently, achieving both high compression ratios and improved speed compared to monolithic tools like CRAM that perform all operations in a single slow process
Solution Approach 2:
The patent performs alignment and classification as preliminary actions before the actual compression step. This allows the compression algorithm to work on pre-organized data with known mismatch patterns, significantly speeding up the compression process while maintaining high compression ratios through targeted encoding strategies
Data Source
AI summary
There is provided an apparatus and a processor-implemented method. The method includes aligning a reference genome with a plurality of DNA sequences. Each of the plurality of DNA sequences has a respective plurality of bases. The method further includes classifying and sorting the plurality of read sequences based on respective numbers of mismatched bases within the plurality of read sequences to obtain a plurality of re-arranged DNA sequences. The method also includes building a histogram based on respective positions of mismatched bases within the plurality of re-arranged DNA sequences. The method additionally includes coding at least some of the plurality of re-arranged DNA sequences based on the histogram.