title: MinSet: a general approach to derive maximally representative database subsets by using fragment dictionaries and its application to the SCOP database.
abstract: BACKGROUND
  The size of current protein databases is a challenge for many Bioinformatics applications, both in terms of processing speed and information redundancy. It may be therefore desirable to efficiently reduce the database of interest to a maximally representative subset.
  
  
  RESULTS
  The MinSet method employs a combination of a Suffix Tree and a Genetic Algorithm for the generation, selection and assessment of database subsets. The approach is generally applicable to any type of string-encoded data, allowing for a drastic reduction of the database size whilst retaining most of the information contained in the original set. We demonstrate the performance of the method on a database of protein domain structures encoded as strings. We used the SCOP40 domain database by translating protein structures into character strings by means of a structural alphabet and by extracting optimized subsets according to an entropy score that is based on a constant-length fragment dictionary. Therefore, optimized subsets are maximally representative for the distribution and range of local structures. Subsets containing only 10% of the SCOP structure classes show a coverage of >90% for fragments of length 1-4.
  
  
  BACKGROUND
  http://mathbio.nimr.mrc.ac.uk/~jkleinj/MinSet.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Dipartimento di Scienze dell'Ambiente e del Territorio, Università degli Studi di Milano-Bicocca, Milano, Italy.
authors.forename: Alessandro
authors.initials: A
authors.lastname: Pandini
authors.forename: Laura
authors.initials: L
authors.lastname: Bonati
authors.forename: Franca
authors.initials: F
authors.lastname: Fraternali
authors.forename: Jens
authors.initials: J
authors.lastname: Kleinjung
mesh_terms: D000465:Algorithms; D044962:Data Compression / Q000379:methods*; D003628:Database Management Systems; D030562:Databases, Protein; D004015:Dictionaries, Chemical as Topic; D010446:Peptide Fragments / Q000737:chemistry / Q000145:classification; D011506:Proteins / Q000737:chemistry* / Q000145:classification*; D020539:Sequence Analysis, Protein / Q000379:methods*; D012984:Software
chemical_list: D010446:Peptide Fragments; D011506:Proteins