title: MetaFam: a unified classification of protein families. I. Overview and statistics.
abstract: BACKGROUND
  Protein sequence classification is becoming an increasingly important means of organizing the voluminous data produced by large-scale genome sequencing projects. At present, there are several independent classification methods. To aid the general classification effort, we have created a unified protein family resource, MetaFam. MetaFam is a protein family classification built upon 10 publicly-accessible protein family databases (Blocks + DOMO, Pfam, PIR-ALN, PRINTS, PROSITE, ProDom, PROTOMAP, SBASE, and SYSTERS). MetaFam's family 'supersets', as we call them, are created automatically using set-theory to compare families among the databases. Families of one database are matched to those in another when the intersection of their members exceeds all other possible family pairings between the two databases. Pairwise family matches are drawn together transitively to create a new list of protein family supersets.
  
  
  RESULTS
  MetaFam family supersets have several useful features: (1) each superset contains more members than the families from which it is composed, because each of the component family databases only works with a subset of our full non-redundant set of proteins; (2) conflicting assignments can be pinpointed quickly, since our analysis identifies individual members that are in conflict with the majority consensus; (3) family descriptions that are absent from automated databases can frequently be assigned; (4) statistics have been computed comparing domain boundaries, family size distributions, and overall quality of MetaFam supersets; (5) the supersets have been loaded into a relational database to allow for complex queries and visualization of the connections among families in a superset and the consensus of individual domain members; and (6) the quality of individual supersets has been assessed using numerous quantitative measures such as family consistency, connectedness, and size. We anticipate this new resource will be particularly useful to genomic database curators.
authors: Computational Biology Centers, Academic Health Center, University of Minnesota, Mayo Mail Code 43, 420 Delaware St SE, Minneapolis, MN 55455-0312, USA.
authors: K A
authors: KA
authors: Silverstein
authors: E
authors: E
authors: Shoop
authors: J E
authors: JE
authors: Johnson
authors: E F
authors: EF
authors: Retzel
mesh_terms: D003627:Data Interpretation, Statistical; D016208:Databases, Factual; D011506:Proteins / Q000145:classification*; D017421:Sequence Analysis
chemical_list: D011506:Proteins