The GESTALT Workbench
Institute for Systems Biology
Large amounts of 'raw' genomic sequence data already exist and continue
to grow exponentially. Many tools are available for automated analysis
of these data by comparison to known sequences or by pattern recognition.
One of the hardest problems is how to present the sequence data and its
derived annotation in an intuitive way. We present here a workbench for
analysis of large-scale genomic sequence data, with strong emphasis on
the production of enriched graphical representation of the analysed data.
The GESTALT Workbench can execute a variety of external analysis programmes (e.g. for gene recognition)
as well as internal analyses (e.g. compositional complexity analysis);
the resulting analysis output files are stored in an internal database.
Integrating the analysis results, a Gestalt* is created for each
sequence. Much biological insight can be obtained at a glance from these
sequence pictorial representations, which prove to be a valuable aid in
quick and intuitive sequence interpretation.
The GESTALT Workbench is designed to pose minimal technical requirements on the user's end.
* Gestalt: (lit. image) a structure, configuration, or pattern of physical,
biological, or psychological phenomena so integrated as to constitute a
functional unit with properties not derivable by summation of its parts.
-- Webster's dictionary
Would you like to see an example?
The GESTALT Workbench is designed to be easy to use from any networked computer. No special browser is required; any graphical WWW browser supporting tables and forms should suffice.
The original GESTALT server is at the Weizmann Institute of Science.
The enhanced GESTALT server is at the Institute for Systems Biology.
To access the GESTALT Workbench, you need a userid. If you do not have one yet, you can:
If you use the GESTALT Workbench in your work and/or publication(s), please quote:
Glusman, G. and Lancet, D. (2000) GESTALT: a workbench for automatic integration and visualization of large-scale genomic sequence analyses. Bioinformatics 16(5): 482-483.
Another relevant reference:
Glusman, G. and Lancet, D. (2001) Visualizing large-scale genomic sequences. IEEE Eng Med Biol Mag 20(4): 49-54.