title: Associative database of protein sequences.
abstract: BACKGROUND
  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.
  
  
  BACKGROUND
  Access to the database is available at http://www.bioinf.mdc-berlin.de/unter2.html++ +
  
  
  BACKGROUND
  (hanke,lehmann,reich)@mdc-berlin.de; bork@embl-heidelberg.de
authors.affiliation: Max-Delbrück-Center for Molecular Medicine, Department of Bioinformatics, Robert-Rössle-Strasse 10, D-13125 Berlin-Buch, Germany.
authors.forename: J
authors.initials: J
authors.lastname: Hanke
authors.forename: G
authors.initials: G
authors.lastname: Lehmann
authors.forename: P
authors.initials: P
authors.lastname: Bork
authors.forename: J G
authors.initials: JG
authors.lastname: Reich
mesh_terms: D000595:Amino Acid Sequence; D000818:Animals; D017124:Conserved Sequence; D016208:Databases, Factual; D006801:Humans; D016247:Information Storage and Retrieval; D008969:Molecular Sequence Data; D016571:Neural Networks, Computer; D011506:Proteins / Q000032:analysis*; D020539:Sequence Analysis, Protein / Q000379:methods*; D012984:Software
chemical_list: D011506:Proteins