The Gene Ontology (GO) knowledgebase (http://geneontology.org) is a comprehensive resource concerning the functions of genes and gene products (proteins and noncoding RNAs). GO annotations cover genes from organisms across the tree of life as well as viruses, though most gene function knowledge currently derives from experiments carried out in a relatively small number of model organisms. Here, we provide an updated overview of the GO knowledgebase, as well as the efforts of the broad, international consortium of scientists that develops, maintains, and updates the GO knowledgebase. The GO knowledgebase consists of three components: (1) the GO-a computational knowledge structure describing the functional characteristics of genes; (2) GO annotations-evidence-supported statements asserting that a specific gene product has a particular functional characteristic; and (3) GO Causal Activity Models (GO-CAMs)-mechanistic models of molecular "pathways" (GO biological processes) created by linking multiple GO annotations using defined relations. Each of these components is continually expanded, revised, and updated in response to newly published discoveries and receives extensive QA checks, reviews, and user feedback. For each of these components, we provide a description of the current contents, recent developments to keep the knowledgebase up to date with new discoveries, and guidance on how users can best make use of the data that we provide. We conclude with future directions for the project.

Cilia are specialized, hair-like structures that project from the cell bodies of eukaryotic cells. With increased understanding of the distribution and functions of various types of cilia, interest in these organelles is accelerating. To effectively use this great expansion in knowledge, this information must be made digitally accessible and available for large-scale analytical and computational investigation. Capture and integration of knowledge about cilia into existing knowledge bases, thus providing the ability to improve comparative genomic data analysis, is the objective of this work.

Use of Protein Ontology in Gene Ontology curation at the Mouse Genome Informatics (MGI) resource.

BACKGROUND:Recent research into ciliary structure and function provides important insights into inherited diseases termed ciliopathies and other cilia-related disorders. This wealth of knowledge needs to be translated into a computational representation to be fully exploitable by the research community. To this end, members of the Gene Ontology (GO) and SYSCILIA Consortia have worked together to improve representation of ciliary substructures and processes in GO.METHODS:Members of the SYSCILIA and Gene Ontology Consortia suggested additions and changes to GO, to reflect new knowledge in the field. The project initially aimed to improve coverage of ciliary parts, and was then broadened to cilia-related biological processes. Discussions were documented in a public tracker. We engaged the broader cilia community via direct consultation and by referring to the literature. Ontology updates were implemented via ontology editing tools.RESULTS:So far, we have created or modified 127 GO terms representing parts and processes related to eukaryotic cilia/flagella or prokaryotic flagella. A growing number of biological pathways are known to involve cilia, and we continue to incorporate this knowledge in GO. The resulting expansion in GO allows more precise representation of experimentally derived knowledge, and SYSCILIA and GO biocurators have created 199 annotations to 50 human ciliary proteins. The revised ontology was also used to curate mouse proteins in a collaborative project. The revised GO and annotations, used in comparative 'before and after' analyses of representative ciliary datasets, improve enrichment results significantly.CONCLUSIONS:Our work has resulted in a broader and deeper coverage of ciliary composition and function. These improvements in ontology and protein annotation will benefit all users of GO enrichment analysis tools, as well as the ciliary research community, in areas ranging from microscopy image annotation to interpretation of high-throughput studies. We welcome feedback to further enhance the representation of cilia biology in GO.

Additional file 7. Papers used to make annotations for Chlamydomonas reinhardtii ciliary genes. Full list of papers with corresponding PubMed IDs that were used for annotations for Chlamydomonas reinhardtii ciliary genes.

The Protein Ontology (PRO) is the reference ontology for proteins in the Open Biomedical Ontologies (OBO) foundry and consists of three sub-ontologies representing protein classes of homologous genes, proteoforms (e.g., splice isoforms, sequence variants, and post-translationally modified forms), and protein complexes. PRO defines classes of proteins and protein complexes, both species-specific and species nonspecific, and indicates their relationships in a hierarchical framework, supporting accurate protein annotation at the appropriate level of granularity, analyses of protein conservation across species, and semantic reasoning. In the first section of this chapter, we describe the PRO framework including categories of PRO terms and the relationship of PRO to other ontologies and protein resources. Next, we provide a tutorial about the PRO website ( proconsortium.org ) where users can browse and search the PRO hierarchy, view reports on individual PRO terms, and visualize relationships among PRO terms in a hierarchical table view, a multiple sequence alignment view, and a Cytoscape network view. Finally, we describe several examples illustrating the unique and rich information available in PRO.

Interest in primary cilia has increased dramatically over the last ten years as it has become clear that ciliopathies are an underlying cause of numerous human diseases including some types of retinitis pigmentosa and polycystic kidney disease. Once thought to be restricted to a few cell types, it is now clear that primary cilia are found on almost all vertebrate cells and are critical to Sonic hedgehog (Shh) signaling. Mouse models play a key role in developing our understanding of the role of primary cilia in control of Shh signaling in development throughout the embryo and in ongoing maintenance of structures such as photoreceptors. To maximize the utility of the wealth of experimental data generated by these mouse ciliopathy models, we have engaged in a project to comprehensively annotate experimentally characterized ciliary genes of the laboratory mouse using Gene Ontology (GO) terms to describe their molecular functions, biological roles, and cellular locations, using the SYSCILIA gold standard of known human ciliary components as a starting point. However, experimental annotations of mouse genes may allow additional genes to be added to the SYSCILIA gold standard list. We have also updated the Gene Ontology to add new terms and clarify existing terms to represent recent advances in our understanding of ciliary biology. These improvements to the representation of cilia within GO greatly improves our ability to make informative annotations for ciliary genes. Comprehensive GO annotation of ciliary genes in the mouse will be a great resource to those doing high-throughput studies or comparative genomic analysis across species.

The Protein Ontology (PRO; http://purl.obolibrary.org/obo/pr) formally defines and describes taxon-specific and taxon-neutral protein-related entities in three major areas: proteins related by evolution; proteins produced from a given gene; and protein-containing complexes. PRO thus serves as a tool for referencing protein entities at any level of specificity. To enhance this ability, and to facilitate the comparison of such entities described in different resources, we developed a standardized representation of proteoforms using UniProtKB as a sequence reference and PSI-MOD as a post-translational modification reference. We illustrate its use in facilitating an alignment between PRO and Reactome protein entities. We also address issues of scalability, describing our first steps into the use of text mining to identify protein-related entities, the large-scale import of proteoform information from expert curated resources, and our ability to dynamically generate PRO terms. Web views for individual terms are now more informative about closely-related terms, including for example an interactive multiple sequence alignment. Finally, we describe recent improvement in semantic utility, with PRO now represented in OWL and as a SPARQL endpoint. These developments will further support the anticipated growth of PRO and facilitate discoverability of and allow aggregation of data relating to protein entities.

A large gap remains between the amount of knowledge in scientific literature and the fraction that gets curated into standardized databases, despite many curation initiatives. Yet the availability of comprehensive knowledge in databases is crucial for exploiting existing background knowledge, both for designing follow-up experiments and for interpreting new experimental data. Structured resources also underpin the computational integration and modeling of regulatory pathways, which further aids our understanding of regulatory dynamics. We argue how cooperation between the scientific community and professional curators can increase the capacity of capturing precise knowledge from literature. We demonstrate this with a project in which we mobilize biological domain experts who curate large amounts of DNA binding transcription factors, and show that they, although new to the field of curation, can make valuable contributions by harvesting reported knowledge from scientific papers. Such community curation can enhance the scientific epistemic process.Database URL: http://www.tfcheckpoint.org.

The Gene Ontology (GO) is an important component of modern biological knowledge representation with great utility for computational analysis of genomic and genetic data. The Gene Ontology Consortium (GOC) consists of a large team of contributors including curation teams from most model organism database groups as well as curation teams focused on representation of data relevant to specific human diseases. Key to the generation of consistent and comprehensive annotations is the development and use of shared standards and measures of curation quality. The GOC engages all contributors to work to a defined standard of curation that is presented here in the context of annotation of genes in the laboratory mouse. Comprehensive understanding of the origin, epistemology, and coverage of GO annotations is essential for most effective use of GO resources. Here the application of comparative approaches to capturing functional data in the mouse system is described.

The Evidence Ontology (ECO) is a structured, controlled vocabulary for capturing evidence in biological research. ECO includes diverse terms for categorizing evidence that supports annotation assertions including experimental types, computational methods, author statements and curator inferences. Using ECO, annotation assertions can be distinguished according to the evidence they are based on such as those made by curators versus those automatically computed or those made via high-throughput data review versus single test experiments. Originally created for capturing evidence associated with Gene Ontology annotations, ECO is now used in other capacities by many additional annotation resources including UniProt, Mouse Genome Informatics, Saccharomyces Genome Database, PomBase, the Protein Information Resource and others. Information on the development and use of ECO can be found at http://evidenceontology.org. The ontology is freely available under Creative Commons license (CC BY-SA 3.0), and can be downloaded in both Open Biological Ontologies and Web Ontology Language formats at http://code.google.com/p/evidenceontology. Also at this site is a tracker for user submission of term requests and questions. ECO remains under active development in response to user-requested terms and in collaborations with other ontologies and database resources. Database URL: Evidence Ontology Web site: http://evidenceontology.org

Transcription factors control which information in a genome becomes transcribed to produce RNAs that function in the biological systems of cells and organisms. Reliable and comprehensive information about transcription factors is invaluable for large-scale network-based studies. However, existing transcription factor knowledge bases are still lacking in well-documented functional information. Here, we provide guidelines for a curation strategy, which constitutes a robust framework for using the controlled vocabularies defined by the Gene Ontology Consortium to annotate specific DNA binding transcription factors (DbTFs) based on experimental evidence reported in literature. Our standardized protocol and workflow for annotating specific DNA binding RNA polymerase II transcription factors is designed to document high-quality and decisive evidence from valid experimental methods. Within a collaborative biocuration effort involving the user community, we are now in the process of exhaustively annotating the full repertoire of human, mouse and rat proteins that qualify as DbTFs in as much as they are experimentally documented in the biomedical literature today. The completion of this task will significantly enrich Gene Ontology-based information resources for the research community. Database URL: www.tfcheckpoint.org

Background Maintaining a bio-ontology in the long term requires improving and updating its contents so that it adequately captures what is known about biological phenomena. This paper illustrates how these processes are carried out, by studying the ways in which curators at the Gene Ontology have hitherto incorporated new knowledge into their resource. Results Five types of circumstances are singled out as warranting changes in the ontology: (1) the emergence of anomalies within GO; (2) the extension of the scope of GO; (3) divergence in how terminology is used across user communities; (4) new discoveries that change the meaning of the terms used and their relations to each other; and (5) the extension of the range of relations used to link entities or processes described by GO terms. Conclusion This study illustrates the difficulties involved in applying general standards to the development of a specific ontology. Ontology curation aims to produce a faithful representation of knowledge domains as they keep developing, which requires the translation of general guidelines into specific representations of reality and an understanding of how scientific knowledge is produced and constantly updated. In this context, it is important that trained curators with technical expertise in the scientific field(s) in question are involved in supervising ontology shifts and identifying inaccuracies.

The Saccharomyces Genome Database (SGD, http://www.yeastgenome.org) is the community resource for the budding yeast Saccharomyces cerevisiae. The SGD project provides the highest-quality manually curated information from peer-reviewed literature. The experimental results reported in the literature are extracted and integrated within a well-developed database. These data are combined with quality high-throughput results and provided through Locus Summary pages, a powerful query engine and rich genome browser. The acquisition, integration and retrieval of these data allow SGD to facilitate experimental design and analysis by providing an encyclopedia of the yeast genome, its chromosomal features, their functions and interactions. Public access to these data is provided to researchers and educators via web pages designed for optimal ease of use.

The Gene Ontology (GO) describes molecular functions, biological processes, and cellular components of gene products using controlled-vocabulary terms that are related to each other in a structure that facilitates computing on GO annotations within and across species. Experimentally-based GO annotations that are manually curated from the literature are often used to predict the functions of related uncharacterized proteins. The accuracy of such annotations is thus critically important, particularly for a well-studied model organism such as Saccharomyces cerevisiae which is frequently used as the source of the experimental data.Comparison of experimentally-based annotations with those predicted by computational methods for the same gene products may reveal inaccuracies in curation of the experimental data, and could additionally be used to evaluate and improve the computational methods. We will present the results of an analysis at SGD that identified four major reasons for discrepancies between the two kinds of annotation. Some discrepancies revealed cases in which human error led to errors or omissions in the manual curation, prompting prioritization for review and correction. In another category, the computational annotations were not supported or were refuted by the literature, thereby suggesting ways in which the accuracy of the prediction methods could be improved. Yet another type of discrepancy resulted from issues with the GO structure, such as missing parentage for certain terms, leading to reexamination and improvement of the ontology. Finally, some discrepancies arose because the computational predictions were entirely novel, and no relevant experimental evidence was available. These cases highlight potential interesting new avenues for experimentation.

AbstractThe Gene Ontology (GO) describes molecular functions, biological processes, and cellular components of gene products using controlled-vocabulary terms that are related to each other in a structure that facilitates computing on GO annotations within and across species. Experimentally-based GO annotations that are manually curated from the literature are often used to predict the functions of related uncharacterized proteins. The accuracy of such annotations is thus critically important, particularly for a well-studied model organism such as Saccharomyces cerevisiae which is frequently used as the source of the experimental data.Comparison of experimentally-based annotations with those predicted by computational methods for the same gene products may reveal inaccuracies in curation of the experimental data, and could additionally be used to evaluate and improve the computational methods. We will present the results of an analysis at SGD that identified four major reasons for discrepancies between the two kinds of annotation. Some discrepancies revealed cases in which human error led to errors or omissions in the manual curation, prompting prioritization for review and correction. In another category, the computational annotations were not supported or were refuted by the literature, thereby suggesting ways in which the accuracy of the prediction methods could be improved. Yet another type of discrepancy resulted from issues with the GO structure, such as missing parentage for certain terms, leading to reexamination and improvement of the ontology. Finally, some discrepancies arose because the computational predictions were entirely novel, and no relevant experimental evidence was available. These cases highlight potential interesting new avenues for experimentation.

The Gene Ontology (GO) Consortium (http://www.geneontology.org) (GOC) continues to develop, maintain and use a set of structured, controlled vocabularies for the annotation of genes, gene products and sequences. The GO ontologies are expanding both in content and in structure. Several new relationship types have been introduced and used, along with existing relationships, to create links between and within the GO domains. These improve the representation of biology, facilitate querying, and allow GO developers to systematically check for and correct inconsistencies within the GO. Gene product annotation using GO continues to increase both in the number of total annotations and in species coverage. GO tools, such as OBO-Edit, an ontology-editing tool, and AmiGO, the GOC ontology browser, have seen major improvements in functionality, speed and ease of use.