dcGO: database of domain-centric ontologies on functions, phenotypes, diseases and more

Abstract

We present ‘dcGO’ (http://supfam.org/SUPERFAMILY/dcGO), a comprehensive ontology database for protein domains. Domains are often the functional units of proteins, thus instead of associating ontological terms only with full-length proteins, it sometimes makes more sense to associate terms with individual domains. Domain-centric GO, ‘dcGO’, provides associations between ontological terms and protein domains at the superfamily and family levels. Some functional units consist of more than one domain acting together or acting at an interface between domains; therefore, ontological terms associated with pairs of domains, triplets and longer supra-domains are also provided. At the time of writing the ontologies in dcGO include the Gene Ontology (GO); Enzyme Commission (EC) numbers; pathways from UniPathway; human phenotype ontology and phenotype ontologies from five model organisms, including plants; anatomy ontologies from three organisms; human disease ontology and drugs from DrugBank. All ontological terms have probabilistic scores for their associations. In addition to associations to domains and supra-domains, the ontological terms have been transferred to proteins, through homology, providing annotations of >80 million sequences covering 2414 complete genomes, hundreds of meta-genomes, thousands of viruses and so forth. The dcGO database is updated fortnightly, and its website provides downloads, search, browse, phylogenetic context and other data-mining facilities.

Untitled

“…dcGO web server was used to predict Genome Ontology (GO), Disease Ontology (DO) and Human Phenotype (HP) from the query protein sequences. 17 KEGG Automatic Annotation Server (KAAS) was used to predict pathway associations from the query protein sequences 18 . Further, homology of 43 sequences (which had DO term) with human proteome were analyzed by NCBI BLASTP program.…”

The Gene Ontology Handbook

“…The recent CATH FunFHMMer web server automates the search procedure for input sequences, resolves multi-domain architectures, assigns each predicted domain to its functional family, and fi nally inherits the GO term annotations found in the library [ 61 ]. The dcGO-short for domain centric-method follows a similar route, but with some key differences [ 62 ]. HMM models are built for both individual domains and supra-domains, i.e., sets of consecutive domains that are defi ned according to the SCOP structural defi nition and the evolutionary one in Pfam [ 63 ].…”

The `dnet¿ approach promotes emerging research on cancer patient survival

“…Knowledge of genes can be: their interacting networks [19], annotations by various ontologies (in dcGO database [27,28]), evolutionary ages [29], and residual domain superfamilies [30]. They are provided as RDataformatted files, and are maintained and updated using in-house Perl scripts.…”