title: Inferring functional modules of protein families with probabilistic topic models.
abstract: BACKGROUND
  Genome and metagenome studies have identified thousands of protein families whose functions are poorly understood and for which techniques for functional characterization provide only partial information. For such proteins, the genome context can give further information about their functional context.
  
  
  RESULTS
  We describe a Bayesian method, based on a probabilistic topic model, which directly identifies functional modules of protein families. The method explores the co-occurrence patterns of protein families across a collection of sequence samples to infer a probabilistic model of arbitrarily-sized functional modules.
  
  
  CONCLUSIONS
  We show that our method identifies protein modules - some of which correspond to well-known biological processes - that are tightly interconnected with known functional interactions and are different from the interactions identified by pairwise co-occurrence. The modules are not specific to any given organism and may combine different realizations of a protein complex or pathway within different taxa.
authors.affiliation: Max Planck Research Group for Computational Genomics and Epidemiology, Max Planck Institute for Informatics, University Campus E1 4, 66123 Saarbrücken, Germany.
authors.forename: Sebastian Ga
authors.initials: SG
authors.lastname: Konietzny
authors.forename: Laura
authors.initials: L
authors.lastname: Dietz
authors.forename: Alice C
authors.initials: AC
authors.lastname: McHardy
mesh_terms: D001499:Bayes Theorem; D005407:Flagella / Q000737:chemistry / Q000378:metabolism; D016678:Genome; D008957:Models, Genetic; D015233:Models, Statistical; D011387:Prokaryotic Cells / Q000378:metabolism; D011506:Proteins / Q000145:classification / Q000235:genetics* / Q000378:metabolism*
chemical_list: D011506:Proteins