FQSqueezer

FQSqueezer compresses FASTQ files using k-mer statistics and algorithms inspired by prediction by partial matching and dynamic Markov coder algorithms to reduce storage requirements of sequencing data generated by modern sequencing instruments.

Key Features:

• FASTQ compression: Compresses sequencing reads stored in FASTQ format to reduce data size.
• K-mer statistics: Leverages k-mer statistics for modeling sequence content.
• PPM- and DMC-inspired algorithm: Employs an algorithm inspired by prediction by partial matching and dynamic Markov coder algorithms for compression.
• Read support: Handles both single-end and paired-end reads of variable lengths.
• High compression ratio: Achieves compression ratios often tens of percent superior to state-of-the-art tools.
• Resource requirements: Requires considerable memory and computational time resources.

Scientific Applications:

• High-throughput sequencing data management: Reduces the storage footprint of sequencing datasets produced by modern sequencing instruments.
• Large-scale genomic studies: Enables more efficient storage for large-scale genomic projects.
• Archival storage: Supports long-term archival of FASTQ data by maximizing compression.
• Storage cost reduction: Lowers storage-related resource demands in sequencing workflows.

Methodology:

Leverages k-mer statistics and an algorithm inspired by prediction by partial matching and dynamic Markov coder algorithms.