pGQL: probabilistic Graphical Query Language

What is pGQL?

pGQL is a software tool in particular for analyzing gene expression time courses. It allows its user to interactively define linear HMM queries on time course data using rectangular graphical widgets called probabilistic time boxes. The analysis is fully interactive and the graphical display shows the time courses along with the graphical query. The results can be submitted to gPROF directly from pGQL.


In order to use pGQL download the pGQL package from here: pGQL.tar.gz

You can unpack this file using the tar and gzip command line utilities by typing on your shell prompt: tar xvzf pGQL.tar.gz (assuming, that your shell prompt is in the directory, where you downloaded pGQL to). pGQL needs no specific installation/compilation routine, but all files contained in the pGQL directory should be kept unmodified and unmoved as they are. To be able to use pGQL please also download the ghmm library and follow the installation instructions provided with it. You find this package here: ghmm .

Using pGQL

Our research paper provides a starting point including a case study to use pGQL. Assuming, that ghmm is available in your Python site-packages all you have to do to use pGQL is open a shell, go into the pGQL directory and type python

Python will present you with a file dialog at startup enabling you to load your data set. We provide a few example data sets in the pGQL folder, so that you can start trying the software at once and inspect the file format needed for pGQL. The specification of the simple file format is the same as for GQL, c.f. GQL documentation .


pGQL is licensed under the GPLv3. You can obtain a copy of the license here GPLv3 .

The icons used in pGQL are from and Crystal Clear and have their own licenses. Check the websites to get licence information or view the entire collection the respective authors offer.

For further information contact Ruben B. Schilling ( This software is a result of or used in the following projects: GQL, GenExpTimecourses, GHMM.


Members: Ruben B. Schilling, Alexander Schliep, Ivan G Costa, Ruben B. Schilling.