title: The predictive power of the CluSTr database.
abstract: CONCLUSIONS
  The CluSTr database employs a fully automatic single-linkage hierarchical clustering method based on a similarity matrix. In order to compute the matrix, first all-against-all pair-wise comparisons between protein sequences are computed using the Smith-Waterman algorithm. The statistical significance of the similarity scores is then assessed using a Monte Carlo analysis, yielding Z-values, which are used to populate the matrix. This paper describes automated annotation experiments that quantify the predictive power and hence the biological relevance of the CluSTr data. The experiments utilized the UniProt data-mining framework to derive annotation predictions using combinations of InterPro and CluSTr. We show that this combination of data sources greatly increases the precision of predictions made by the data-mining framework, compared with the use of InterPro data alone. We conclude that the CluSTr approach to clustering proteins makes a valuable contribution to traditional protein classifications.
  
  
  BACKGROUND
  http://www.ebi.ac.uk/clustr/.
authors.affiliation: EMBL Outstation Hinxton, The European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK.
authors.forename: Robert
authors.initials: R
authors.lastname: Petryszak
authors.forename: Ernst
authors.initials: E
authors.lastname: Kretschmann
authors.forename: Daniela
authors.initials: D
authors.lastname: Wieser
authors.forename: Rolf
authors.initials: R
authors.lastname: Apweiler
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D016000:Cluster Analysis; D019295:Computational Biology / Q000295:instrumentation / Q000379:methods*; D016208:Databases, Factual; D030541:Databases, Genetic; D030562:Databases, Protein; D006801:Humans; D020407:Internet; D009010:Monte Carlo Method; D040901:Proteomics / Q000379:methods; D020539:Sequence Analysis, Protein