MOTIVATION: We present a new concept that combines data storage and data analysis in genome research, based on an associative network memory. As an illustration, 115 000 conserved regions from over 73 000 published sequences (i.e. from the entire annotated part of the SWISSPROT sequence database) were identified and clustered by a self-organizing network. Similarity and kinship, as well as degree of distance between the conserved protein segments, are visualized as neighborhood relationship on a two-dimensional topographical map. RESULTS: Such a display overcomes the restrictions of linear list processing and allows local and global sequence relationships to be studied visually. Families are memorized as prototype vectors of conserved regions. On a massive parallel machine, clustering and updating of the database take only a few seconds; a rapid analysis of incoming data such as protein sequences or ESTs is carried out on present-day workstations. AVAILABILITY: Access to the database is available at http://www.bioinf.mdc-berlin.de/unter2.html++ + CONTACT: (hanke,lehmann,reich)@mdc-berlin.de; [email protected]

The method of Kohonen maps, a special form of neural networks, was applied as a visualization tool for the analysis of protein sequence similarity. The procedure converts sequence (domains, aligned sequences, segments of secondary structure) into a characteristic signal matrix. This conversion depends on the property or replacement score vector selected by the user. Similar sequences have small distance in the signal space. The trained Kohonen network is functionally equivalent to an unsupervised non-linear cluster analyzer. Protein families, or aligned sequences, or segments of similar secondary structure, aggregate as clusters, and their proximity may be inspected on a color screen or on paper. Pull-down menus permit access to background information in the established text-oriented way.