title: A domain-centric solution to functional genomics via dcGO Predictor.
abstract: BACKGROUND
  Computational/manual annotations of protein functions are one of the first routes to making sense of a newly sequenced genome. Protein domain predictions form an essential part of this annotation process. This is due to the natural modularity of proteins with domains as structural, evolutionary and functional units. Sometimes two, three, or more adjacent domains (called supra-domains) are the operational unit responsible for a function, e.g. via a binding site at the interface. These supra-domains have contributed to functional diversification in higher organisms. Traditionally functional ontologies have been applied to individual proteins, rather than families of related domains and supra-domains. We expect, however, to some extent functional signals can be carried by protein domains and supra-domains, and consequently used in function prediction and functional genomics.
  
  
  RESULTS
  Here we present a domain-centric Gene Ontology (dcGO) perspective. We generalize a framework for automatically inferring ontological terms associated with domains and supra-domains from full-length sequence annotations. This general framework has been applied specifically to primary protein-level annotations from UniProtKB-GOA, generating GO term associations with SCOP domains and supra-domains. The resulting 'dcGO Predictor', can be used to provide functional annotation to protein sequences. The functional annotation of sequences in the Critical Assessment of Function Annotation (CAFA) has been used as a valuable opportunity to validate our method and to be assessed by the community. The functional annotation of all completely sequenced genomes has demonstrated the potential for domain-centric GO enrichment analysis to yield functional insights into newly sequenced or yet-to-be-annotated genomes. This generalized framework we have presented has also been applied to other domain classifications such as InterPro and Pfam, and other ontologies such as mammalian phenotype and disease ontology. The dcGO and its predictor are available at http://supfam.org/SUPERFAMILY/dcGO including an enrichment analysis tool.
  
  
  CONCLUSIONS
  As functional units, domains offer a unique perspective on function prediction regardless of whether proteins are multi-domain or single-domain. The 'dcGO Predictor' holds great promise for contributing to a domain-centric functional understanding of genomes in the next generation sequencing era.
authors.affiliation: Department of Computer Science, University of Bristol, The Merchant Venturers Building, Bristol BS8 1UB, UK. hfang@cs.bris.ac.uk
authors.forename: Hai
authors.initials: H
authors.lastname: Fang
authors.forename: Julian
authors.initials: J
authors.lastname: Gough
mesh_terms: D005796:Genes; D016678:Genome; D023281:Genomics / Q000379:methods*; D058977:Molecular Sequence Annotation; D010641:Phenotype; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry / Q000235:genetics; D012984:Software; D018875:Vocabulary, Controlled
chemical_list: D011506:Proteins