title: Family relationships: should consensus reign?--consensus clustering for protein families.
abstract: BACKGROUND
  Reliable identification of protein families is key to phylogenetic analysis, functional annotation and the exploration of protein function diversity in a given phylogenetic branch. As more and more complete genomes are sequenced, there is a need for powerful and reliable algorithms facilitating protein families construction.
  
  
  RESULTS
  We have formulated the problem of protein families construction as an instance of consensus clustering, for which we designed a novel algorithm that is computationally efficient in practice and produces high quality results. Our algorithm uses an election method to construct consensus families from competing clustering computations. Our consensus clustering algorithm is tailored to serve the specific needs of comparative genomics projects. First, it provides a robust means to incorporate results from different and complementary clustering methods, thus avoiding the need for an a priori choice that may introduce computational bias in the results. Second, it is suited to large-scale projects due to the practical efficiency. And third, it produces high quality results where families tend to represent groupings by biological function.
  
  
  BACKGROUND
  This method has been used for Génolevures project to compute protein families of Hemiascomycetous yeasts. The data are available online at http://cbi.labri.fr/Genolevures/fam/
authors.affiliation: CNRS/LaBRI, Université Bordeaux 1 351 cours de la Libération, 33405 Talence Cedex, France. macha@labri.fr
authors.forename: Macha
authors.initials: M
authors.lastname: Nikolski
authors.forename: David J
authors.initials: DJ
authors.lastname: Sherman
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D001203:Ascomycota / Q000235:genetics / Q000378:metabolism*; D016000:Cluster Analysis; D016384:Consensus Sequence; D005656:Fungal Proteins / Q000737:chemistry* / Q000235:genetics / Q000378:metabolism*; D008969:Molecular Sequence Data; D005810:Multigene Family / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*
chemical_list: D005656:Fungal Proteins