Sequence Read Compression Using Duplicate-Aware FASTA/FASTQ Storage
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for compressing sequencing data generated by next-generation sequencing technologies are inadequate, resulting in large file sizes that pose storage and transfer bottlenecks, limiting the ability of clinics and labs to perform genetic carrier screening and other research services.
Innovation Solution
A method that identifies and stores only unique sequence information from redundant sequence read files, reducing storage space and transfer costs by creating lossless compressed files that can be easily human-readable and processed, using algorithms that identify duplicative reads in FASTA and FASTQ formats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If existing compression methods are used on sequencing data, then file size is reduced, but the compressed files become binary and lossy or are wrapped into specialized programs making them inaccessible
Solution Approach 1:
The patent extracts only the essential unique sequence information from redundant sequencing reads, removing duplicate data while preserving the core scientific value. This extraction approach reduces file size while maintaining data integrity and human readability, avoiding the pitfalls of binary compression formats.
Solution Approach 2:
The patent changes the representation parameters of sequencing data by storing only unique sequences with metadata instead of all raw reads. This parameter transformation maintains data accessibility while achieving compression, as the reduced dataset uses the same text-based formats (FASTA/FASTQ) that researchers can directly read and analyze.
2Reliability
If all sequence reads are stored to maintain data integrity, then data completeness is preserved, but storage costs and transfer bandwidth requirements increase significantly
Solution Approach 1:
The patent creates a compressed representation by storing unique sequences as copies of reference data, with metadata indicating which reads correspond to which unique sequences. This copying strategy maintains data completeness for analysis purposes while dramatically reducing storage requirements by eliminating redundant duplicate reads.
Solution Approach 2:
The patent performs preliminary identification and removal of duplicate sequences before storage, anticipating future analysis needs. By pre-processing the data to retain only unique sequences with appropriate metadata, the system ensures data completeness is maintained for downstream analysis while storage space is optimized from the outset.
3Adaptability or versatility
If compression algorithms are integrated into specialized alignment programs, then compression functionality is available, but the tools become complex and difficult to use independently
Solution Approach 1:
The patent segments the compression functionality into a standalone preprocessing step that operates independently of alignment and analysis tools. By separating the compression function into its own module that outputs standard FASTA/FASTQ formats, the system provides compression capability without increasing the complexity of downstream analysis tools, maintaining their simplicity and independence.
Solution Approach 2:
The patent creates a universal compression approach that works with standard sequencing data formats and outputs compatible with any downstream analysis tool. This universal interface allows the compression functionality to be integrated into various workflows without requiring specialized programs, thereby reducing overall system complexity while maintaining versatility.
Data Source
AI summary
The present invention generally relates to storing sequence read data. The invention can involve obtaining a plurality of sequence reads from a sample, identifying one or more sets of duplicative sequence reads within the plurality of sequence reads, and storing only one of the sequence reads from each set of duplicative sequence reads in a text file using nucleotide characters.