Skip to content
/ LCQS Public

Robust and Efficient Lossless Compression of Quality Scores in FASTQ Files with Random Access Decompression Functionality

Notifications You must be signed in to change notification settings

SCUT-CCNL/LCQS

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

19 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

LCQS

Cite

This software implements the LCQS algorithm described in: Fu J , Ke B , Dong S . “LCQS: an efficient lossless compression tool of quality scores with random access functionality” [J]. BMC Bioinformatics, 2020, 21(1).

Building

LCQS is compiled on 64 bit Linux with g++ and C++11 standard. To build it, run the following command in the main directory:

make

If building successfully, the executable file lcqs will be generated in the same directory of the source code.

Usage of LCQS

Compress

lcqs c <input-file> <output-file>

In compression mode, LCQS takes a file consists of quality scores of FASTQ format as input.

Decompress

lcqs d <input-file> <output-file>

In decompression mode, LCQS takes a compressed LCQS file as input.

Random access

lcqs r <input-file> <output-file> <first-line> <last-line>

In random-access mode, LCQS takes a compressed LCQS file as input, and retrieves uncompressed contents as output, from the first line (inclusive) to the last line (inclusive) specified in the parameters.

Example

An Example is presented with a sample file sample.in.

lcqs c sample.in sample.lcqs

Compress the file sample.in and output to the file sample.lcqs.

lcqs d sample.lcqs sample.in

Decompress the file sample.lcqs and output to the file sample.in.

lcqs r sample.lcqs sample.part 10 100

Fetch the original content from 10th line to 100th line.

LIBZPAQ

LIBZPAQ is a public domain API of Matt Mahoney's ZPAQ project. The library used in this project is a optimized version based on LIBZPAQ of zpaq 7.15. For detailed descriptions of LIBZPAQ, refer to ZPAQ.

LIBZPAQ is optimized with vector instruction sets. The context model computations are time-consuming when predicting bits and updating parameters, so we rewrite several parts of them with vector instructions.

To use the optimized version, you need to replace the file libzpaq.cpp with the one of this project. Note that for JIT version, you can compile it with the same instructions. But for NOJIT version, you should replace the option "-msse2" with "-msse4".

About

Robust and Efficient Lossless Compression of Quality Scores in FASTQ Files with Random Access Decompression Functionality

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published