Welcome to The GESTALT Workbench

Abstract

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 (GEnomic Sequence Total Analysis and Lookup Tool) 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. In addition, sequences can be compared in pairwise fashion and visualised by content-annotated, bidirectional dotplots.

* 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

Accessing the GESTALT Workbench

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.

GESTALT was originally developed at the Weizmann Institute of Science.

It is now accessible at the Institute for Systems Biology.

Quoting the GESTALT Workbench

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.