Quality Value Zip (qvz) is a lossy compression algorithm for storing quality values associated with DNA sequencing. This software implements the qvz algorithm for both encoding and decoding.
qvz can be used on Windows, Linux, or Mac. Currently we only provide a source distribution. qvz has no external dependencies, linking only against libc, libm, and librt.
The distribution is configured out of the box for linux. To build on a mac, copy src/Makefile.apple to
replace src/Makefile. Build with make in the toplevel folder. You are responsible for installing the
binary in an appropriate system-wide location (i.e. /usr/bin) if you wish.
There is currently no makefile for Windows. A visual studio project can be made (and testing has been done on windows to verify compatibility) but the steps are beyond the scope of this guide, because you must take several additional steps to guarantee a sane environment on windows (such as replacing stdint and inttypes with correct versions). For this reason we also currently do not distribute a windows build script.
qvz is used from the command line. The general invocation is:
qvz (options) [input file] [output file]
Input and output must be files, currently it does not process standard input or output. The input file must be a file consisting only of quality scores, with one read per line. Thus, the input would consist of every fourth line in a FASTQ file. The other three lines must be compressed separately.
Available options are:
Operating Mode:
-q Compress the quality score input file (default on)
-x Extract quality values from input file
Compression Parameters:
-f [ratio] Compress using a variable allocation of [ratio] bits per bit of input entropy per symbol
-r [rate] Compress using a fixed allocation of [rate] bits per symbol
-d [M|L|A] Compress while optimizing for MSE, Log(1+L1), or L1 distortions, respectively (default: MSE)
Clustering Parameters:
-c [#] Compress using # clusters. Going above 5 is not recommended due to computational complexity (default: 1)
-T [#] Use # as a threshold for cluster centroid movement distance before declaring an approximate clustering as "good enough"
Extra Options:
-h Print help summary
-v Enable verbose progress output
-s Print summary stats to STDOUT after compression (independent of -v)
-u [file] Write the quantized but not compressed values of [file] (default: off)
qvz uses the approach described in a paper submitted to Bioinformatics to perform lossy compression. Data is clustered to reduce global variability, then each cluster is compressed by calculating a set of quantization matrices that performs optimally under the chosen distortion metric and the empirical statistics of the data, using a first order Markov prediction model.
qvz is available under the terms of the GPLv3. See COPYING for more information.
Please use GitHub issues to open bug reports or provide feedback about qvz.
qvz was created by Greg Malysa, Mikel Hernaez, Idoia Ochoa, Milind Rao, and Karthik Ganesan at Stanford University.