FastqCLS: a FASTQ compressor for long-read sequencing via read reordering using a novel scoring model

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Genome sequencing generates massive datasets, demanding significant storage and causing analysis bottlenecks.
• Existing FASTQ compression methods are often suboptimal and primarily designed for short-read data.
• The increasing dominance of long-read sequencing highlights the need for specialized compression tools.

Purpose of the Study:

• To develop a novel, lossless compression algorithm for FASTQ files, specifically addressing the challenges of long-read sequencing data.
• To create an accessible software package, FastqCLS, for efficient FASTQ data compression.
• To evaluate the performance of FastqCLS against existing compression tools using benchmark and long-read datasets.

Main Methods:

• Designed a compression algorithm based on read reordering and a novel scoring model.
• Integrated the algorithm into a user-friendly software package named FastqCLS.
• Provided FastqCLS as a Docker image for simplified installation and execution.
• Validated the method using benchmark datasets, including newly generated long-read sequencing data.

Main Results:

• FastqCLS achieves significant reduction in FASTQ file size for long-read data.
• The algorithm demonstrates superior compression ratios compared to existing major FASTQ compression tools.
• No information loss was incurred during the compression process.

Conclusions:

• FastqCLS offers an effective solution for compressing long-read sequencing data, mitigating storage and transfer issues.
• The software package provides an accessible and efficient tool for the genomics community.
• This advancement supports the growing demands of large-scale genomic data analysis.