Tony Sawford

The UniProt-GO Annotation database in 2011

The GO annotation dataset provided by the UniProt Consortium (GOA: http://www.ebi.ac.uk/GOA) is a comprehensive set of evidenced-based associations between terms from the Gene Ontology resource and UniProtKB proteins. Currently supplying over 100 million annotations to 11 million proteins in more than 360 000 taxa, this resource has increased 2-fold over the last 2 years and has benefited from a wealth of checks to improve annotation correctness and consistency as well as now supplying a greater information content enabled by GO Consortium annotation format developments. Detailed, manual GO annotations obtained from the curation of peer-reviewed papers are directly contributed by all UniProt curators and supplemented with manual and electronic annotations from 36 model organism and domain-focused scientific resources. The inclusion of high-quality, automatic annotation predictions ensures the UniProt GO annotation dataset supplies functional information to a wide range of proteins, including those from poorly characterized, non-model organism species. UniProt GO annotations are freely available in a range of formats accessible by both file downloads and web-based views. In addition, the introduction of a new, normalized file format in 2010 has made for easier handling of the complete UniProt-GOA data set.

The GOA database: gene ontology annotation updates for 2015

The Gene Ontology Annotation (GOA) resource (http://www.ebi.ac.uk/GOA) provides evidence-based Gene Ontology (GO) annotations to proteins in the UniProt Knowledgebase (UniProtKB). Manual annotations provided by UniProt curators are supplemented by manual and automatic annotations from model organism databases and specialist annotation groups. GOA currently supplies 368 million GO annotations to almost 54 million proteins in more than 480 000 taxonomic groups. The resource now provides annotations to five times the number of proteins it did 4 years ago. As a member of the GO Consortium, we adhere to the most up-to-date Consortium-agreed annotation guidelines via the use of quality control checks that ensures that the GOA resource supplies high-quality functional information to proteins from a wide range of species. Annotations from GOA are freely available and are accessible through a powerful web browser as well as a variety of annotation file formats.

Understanding how and why the Gene Ontology and its annotations evolve: the GO within UniProt

The Gene Ontology Consortium (GOC) is a major bioinformatics project that provides structured controlled vocabularies to classify gene product function and location. GOC members create annotations to gene products using the Gene Ontology (GO) vocabularies, thus providing an extensive, publicly available resource. The GO and its annotations to gene products are now an integral part of functional analysis, and statistical tests using GO data are becoming routine for researchers to include when publishing functional information. While many helpful articles about the GOC are available, there are certain updates to the ontology and annotation sets that sometimes go unobserved. Here we describe some of the ways in which GO can change that should be carefully considered by all users of GO as they may have a significant impact on the resulting gene product annotations, and therefore the functional description of the gene product, or the interpretation of analyses performed on GO datasets. GO annotations for gene products change for many reasons, and while these changes generally improve the accuracy of the representation of the underlying biology, they do not necessarily imply that previous annotations were incorrect. We additionally describe the quality assurance mechanisms we employ to improve the accuracy of annotations, which necessarily changes the composition of the annotation sets we provide. We use the Universal Protein Resource (UniProt) for illustrative purposes of how the GO Consortium, as a whole, manages these changes.

A method for increasing expressivity of Gene Ontology annotations using a compositional approach

BackgroundThe Gene Ontology project integrates data about the function of gene products across a diverse range of organisms, allowing the transfer of knowledge from model organisms to humans, and enabling computational analyses for interpretation of high-throughput experimental and clinical data. The core data structure is the annotation, an association between a gene product and a term from one of the three ontologies comprising the GO. Historically, it has not been possible to provide additional information about the context of a GO term, such as the target gene or the location of a molecular function. This has limited the specificity of knowledge that can be expressed by GO annotations.ResultsThe GO Consortium has introduced annotation extensions that enable manually curated GO annotations to capture additional contextual details. Extensions represent effector–target relationships such as localization dependencies, substrates of protein modifiers and regulation targets of signaling pathways and transcription factors as well as spatial and temporal aspects of processes such as cell or tissue type or developmental stage. We describe the content and structure of annotation extensions, provide examples, and summarize the current usage of annotation extensions.ConclusionsThe additional contextual information captured by annotation extensions improves the utility of functional annotation by representing dependencies between annotations to terms in the different ontologies of GO, external ontologies, or an organism’s gene products. These enhanced annotations can also support sophisticated queries and reasoning, and will provide curated, directional links between many gene products to support pathway and network reconstruction.

The Impact of Focused Gene Ontology Curation of Specific Mammalian Systems

The Gene Ontology (GO) resource provides dynamic controlled vocabularies to provide an information-rich resource to aid in the consistent description of the functional attributes and subcellular locations of gene products from all taxonomic groups (www.geneontology.org). System-focused projects, such as the Renal and Cardiovascular GO Annotation Initiatives, aim to provide detailed GO data for proteins implicated in specific organ development and function. Such projects support the rapid evaluation of new experimental data and aid in the generation of novel biological insights to help alleviate human disease. This paper describes the improvement of GO data for renal and cardiovascular research communities and demonstrates that the cardiovascular-focused GO annotations, created over the past three years, have led to an evident improvement of microarray interpretation. The reanalysis of cardiovascular microarray datasets confirms the need to continue to improve the annotation of the human proteome. Availability GO annotation data is freely available from: ftp://ftp.geneontology.org/pub/go/gene-associations/

Use of Gene Ontology Annotation to understand the peroxisome proteome in humans

The Gene Ontology (GO) is the de facto standard for the functional description of gene products, providing a consistent, information-rich terminology applicable across species and information repositories. The UniProt Consortium uses both manual and automatic GO annotation approaches to curate UniProt Knowledgebase (UniProtKB) entries. The selection of a protein set prioritized for manual annotation has implications for the characteristics of the information provided to users working in a specific field or interested in particular pathways or processes. In this article, we describe an organelle-focused, manual curation initiative targeting proteins from the human peroxisome. We discuss the steps taken to define the peroxisome proteome and the challenges encountered in defining the boundaries of this protein set. We illustrate with the use of examples how GO annotations now capture cell and tissue type information and the advantages that such an annotation approach provides to users. Database URL: http://www.ebi.ac.uk/GOA/ and http://www.uniprot.org

Using the Gene Ontology to Annotate Key Players in Parkinson’s Disease

The Gene Ontology (GO) is widely recognised as the gold standard bioinformatics resource for summarizing functional knowledge of gene products in a consistent and computable, information-rich language. GO describes cellular and organismal processes across all species, yet until now there has been a considerable gene annotation deficit within the neurological and immunological domains, both of which are relevant to Parkinson’s disease. Here we introduce the Parkinson’s disease GO Annotation Project, funded by Parkinson’s UK and supported by the GO Consortium, which is addressing this deficit by providing GO annotation to Parkinson’s-relevant human gene products, principally through expert literature curation. We discuss the steps taken to prioritise proteins, publications and cellular processes for annotation, examples of how GO annotations capture Parkinson’s-relevant information, and the advantages that a topic-focused annotation approach offers to users. Building on the existing GO resource, this project collates a vast amount of Parkinson’s-relevant literature into a set of high-quality annotations to be utilized by the research community.

Expanding the horizons of microRNA bioinformatics

MicroRNA regulation of key biological and developmental pathways is a rapidly expanding area of research, accompanied by vast amounts of experimental data. This data, however, is not widely available in bioinformatic resources, making it difficult for researchers to find and analyze microRNA-related experimental data and define further research projects. We are addressing this problem by providing two new bioinformatics data sets that contain experimentally verified functional information for mammalian microRNAs involved in cardiovascular-relevant, and other, processes. To date, our resource provides over 4400 Gene Ontology annotations associated with over 500 microRNAs from human, mouse, and rat and over 2400 experimentally validated microRNA:target interactions. We illustrate how this resource can be used to create microRNA-focused interaction networks with a biological context using the known biological role of microRNAs and the mRNAs they regulate, enabling discovery of associations between gene products, biological pathways and, ultimately, diseases. This data will be crucial in advancing the field of microRNA bioinformatics and will establish consistent data sets for reproducible functional analysis of microRNAs across all biological research areas.