The Gene Ontology Annotation (GOA) Database

Abstract

AbstractThe Gene Ontology (GO) is a well-established, structured vocabulary that has been successfully used for 10 years in the annotation of proteins. GO terms, created in consultation with the biology community, are used to replace the multiple nomenclatures used by scientific databases that can hamper data integration. Currently GO consists of more than 26,500 terms distributed over three ontologies that describe the molecular function, biological process and subcellular location of a protein in a generic cell.The Gene Ontology Annotation (GOA) database ("http://www.ebi.ac.uk/GOA":http://www.ebi.ac.uk/GOA) aims to provide high-quality manual and electronic GO annotations to proteins within the UniProt Knowledgebase (UniProtKB). By annotating all ‘known’ proteins with GO terms and transferring this knowledge to highly similar ‘unknown’ proteins, GOA offers a valuable contribution to the understanding of all proteomes.As well as generating manual annotation, made by extracting experimental evidence from full text peer-reviewed publications, GOA produces electronic annotation by making large-scale assignments of GO terms to proteins using computational methods. To date we have six electronic annotation methods including; InterPro2GO, Swiss-Prot Keyword2GO and the projection of annotations between orthologous species using Ensembl Compara.GOA provides annotated entries for over 180,000 species and is the largest and most comprehensive open-source contributor of annotations to the GO Consortium annotation effort. In addition, by integrating GO annotations from model organism groups (e.g. FlyBase, GeneDB, MGI, RGD, SGD and TAIR), GOA ensures the dataset remains a key reference. GOA prioritises the annotation of the human proteome and provides this annotation to the GO Consortium’s Reference Genome project.GOA produces monthly releases of annotations to the human, mouse, rat, zebrafish, cow, chicken and Arabidopsis proteomes as well as a file for the multiple species within UniProtKB. The GOA dataset can be queried through a user-friendly web interface via our QuickGO browser("http://www.ebi.ac.uk/QuickGO":http://www.ebi.ac.uk/QuickGO) or downloaded in a parsable format via the EBI("ftp://ftp.ebi.ac.uk/pub/databases/GO/goa":ftp://ftp.ebi.ac.uk/pub/databases/GO/goa) and GO FTP sites. The GOA dataset has increasingly been integrated into tools that aid in the analysis of large datasets resulting from high-throughput experiments thus assisting researchers in biological interpretation of their results.