The robust FASTQ-specific compressor for recent generation data via score-based reordering.
FastqCLS has no external dependencies, using only linux commands and general compressor zpaq.
FastqCLS can be used on docker hub.
docker pull krlucete/fastqcls:1.3
docker container run -itd --name [Your Container Name] krlucete/fastqcls:1.3
docker attach [Your Container Name]
cd zpaq-master
make clean
make install
FastqCLS is used from the command line. Please make sure to move the file to the same path as the command.
The general compression command is:
python3 cls.py (options) -i [input file]
The general decompression command is:
python3 cls_decomp.py (options) -i [input file]
If you wish, you can use the pypy compiler.
pypy cls.py (options) -i [input file]
pypy cls_decomp.py (options) -i [input file]
Input and output must be files, currently it does not process standard input or output.
Available options are:
Operating Mode:
-p [#] Use # as the number of threads on general compressor. (default : 8)
Use 0 if you wish to use the number of prcessor cores.
Checking Parameters:
-sT [#] Use # as a threshold of letter percentage on raw sequence (default : 5)
-qT [#] Use # as a threshold of letter percentage on quality (default : 1)
Extra Options:
-h Print help summary
FastqCLS uses the approach described in a paper submitted to Bioinformatics to perform lossless compression. Data is reordering by the letter percentage to improve entropy.
FastqCLS is available under the terms of the MIT. See COPYING for more information.
Please use GitHub issues to open bug reports or provide feedback about FastqCLS.
FastqCLS was created by DoHyeon Lee at Pusan National University.