FCLQC

FCLQC compresses quality scores in FASTQ files to provide fast, concurrent, lossless compression for storage and analysis of sequencing data.


Key Features:

  • Lossless Compression: Ensures lossless compression of FASTQ quality scores, preserving original data for clinical and archival use.
  • Fast Compression and Decompression: Demonstrates at least a 31x improvement in compression speed versus LCQS with up to 13.58% reduction in compression ratio (average 8.26%), and compresses approximately 3x faster than 7-zip while maintaining superior compression ratios.
  • Random Access Functionality: Supports random access to enable retrieval of specific data segments without full-file decompression.
  • Concurrency and Scalability: Implemented in Rust and uses concurrent programming to utilize multiple threads, scaling near-linearly with available cores.
  • Practical Application: Optimized speed makes it suitable for time-sensitive workflows such as real-time data analysis and large-scale genomic studies.

Scientific Applications:

  • Clinical and archival storage: Preserves exact quality scores required for clinical applications and long-term archival storage.
  • Real-time data analysis: Reduces compression and decompression time for workflows that require rapid turnaround.
  • Large-scale genomic studies: Improves throughput and storage efficiency for large sequencing projects.
  • Selective data retrieval: Random-access support enables efficient extraction of specific segments from large FASTQ datasets.

Methodology:

Implements a lossless compression method targeting FASTQ quality scores, leverages concurrent programming implemented in Rust to utilize multiple threads, and provides random access to compressed data.

Topics

Details

License:
MIT
Cost:
Free of charge
Tool Type:
command-line tool
Operating Systems:
Mac, Linux, Windows
Programming Languages:
Other
Added:
6/7/2022
Last Updated:
6/7/2022

Operations

Publications

Cho M, No A. FCLQC: fast and concurrent lossless quality scores compressor. BMC Bioinformatics. 2021;22(1). doi:10.1186/s12859-021-04516-7. PMID:34930110. PMCID:PMC8686598.