Quality Score Encoding for Faster Nucleic Acid Data Compression
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Solution Overview
Problem
Current methods for compressing quality scores from nucleic acid sequencing data are inefficient, leading to slower compression speeds and lower compression ratios, which result in larger file sizes and increased storage and transmission costs.
Innovation Solution
The proposed solution involves a multi-stage compression method that initially encodes quality scores using base-x encoding, where x is an integer representing the number of unique quality scores, followed by further encoding using techniques like Prediction by Partial Matching (PPMD) to reduce the data size before inputting it into a compression engine, thereby achieving faster compression speeds and higher compression ratios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If current compression methods are used for quality scores, then the compression process is simple, but the compression speed is slow and compression ratio is low
Solution Approach 1:
The patent segments the compression process into multiple distinct stages: base-x encoding stage, PPMD encoding stage, and final compression stage. Each stage processes the data in a specific way, with the base-x encoding reducing quality scores to fewer bits, PPMD encoding further compressing the sequence, and the final stage producing the compressed output. This segmentation allows each stage to be optimized independently, improving overall compression speed and ratio while maintaining manageable complexity through modular design.
Solution Approach 2:
The patent applies preliminary encoding actions before the main compression process. Specifically, base-x encoding is performed first to convert quality scores into a more compact representation, and PPMD encoding is applied as a preliminary step to further reduce the data size before final compression. These preliminary actions prepare the data in advance, making the subsequent compression more efficient and achieving higher compression ratios.
2Quantity of substance
If current compression methods are used for quality scores, then the implementation is straightforward, but the file size after compression is large
Solution Approach 1:
The compression process is divided into multiple encoding stages that progressively reduce file size. The base-x encoding stage converts each quality score into a compact x-bit representation, achieving immediate size reduction. The PPMD encoding stage further compresses the sequence by exploiting statistical patterns. This segmented approach achieves significant file size reduction while keeping each individual encoding stage relatively simple and manageable.
Solution Approach 2:
The patent changes the representation parameters of quality scores through base-x encoding, where x is typically 3 or 4. This parameter change transforms the standard quality score format into a more compact form that uses fewer bits per score. Additionally, the PPMD encoding changes the statistical parameters of the data representation, achieving further compression by modeling the probability distribution of quality scores.
3Loss of energy
If current compression methods are used for quality scores, then the processing is simple, but the transmission and storage costs are high
Solution Approach 1:
The multi-stage encoding process segments the compression task into base-x encoding, PPMD encoding, and final compression stages. This segmentation achieves superior compression ratios, resulting in smaller file sizes that require less storage space and less bandwidth for transmission. Although the process is more complex, the reduction in file size leads to significant decreases in storage and transmission costs, making the approach economically beneficial.
Solution Approach 2:
The preliminary encoding stages (base-x and PPMD) prepare the data in advance to achieve maximum compression before storage or transmission. By performing these encoding actions beforehand, the patent ensures that the final compressed file is as small as possible, thereby minimizing subsequent storage requirements and transmission costs. This preliminary action approach optimizes the data representation before it enters the storage or transmission pipeline.
Data Source
AI summary
Methods, systems, and computer programs for compressing nucleic acid sequence data. A method can include obtaining nucleic acid sequence data representing: (i) a read sequence, and (ii) a plurality of quality scores, determining whether the read sequence includes at least one “N” base, based on a determination that the read sequence includes at least one “N” base, generating, by one or more computers, a first encoding data set by using a first encoding process to encode each set of four quality scores of the read sequence into a single byte of memory, and using a second encoding process to encode the first encoded data set, thereby compressing the data to be compressed.