Gene ontology annotations and resources
Mississippi State University1, Lawrence Berkeley National Laboratory2, Northwestern University3, Texas A&M University4, University of Cambridge5, Swiss Institute of Bioinformatics6, University College London7, University of Maryland, Baltimore8, European Bioinformatics Institute9, Medical College of Wisconsin10, New York University11, Stanford University12, Carnegie Institution for Science13, University of Southern California14, California Institute of Technology15, University of Oregon16
TL;DR: The Gene Ontology (GO) Consortium is a community-based bioinformatics resource that classifies gene product function through the use of structured, controlled vocabularies and has been expanded not only to cover new areas of biology through focused interaction with experts, but also to capture greater specificity in all areas of the ontology.
Abstract: The Gene Ontology (GO) Consortium (GOC, http://www.geneontology.org) is a community-based bioinformatics resource that classifies gene product function through the use of structured, controlled vocabularies. Over the past year, the GOC has implemented several processes to increase the quantity, quality and specificity of GO annotations. First, the number of manual, literature-based annotations has grown at an increasing rate. Second, as a result of a new 'phylogenetic annotation' process, manually reviewed, homology-based annotations are becoming available for a broad range of species. Third, the quality of GO annotations has been improved through a streamlined process for, and automated quality checks of, GO annotations deposited by different annotation groups. Fourth, the consistency and correctness of the ontology itself has increased by using automated reasoning tools. Finally, the GO has been expanded not only to cover new areas of biology through focused interaction with experts, but also to capture greater specificity in all areas of the ontology using tools for adding new combinatorial terms. The GOC works closely with other ontology developers to support integrated use of terminologies. The GOC supports its user community through the use of e-mail lists, social media and web-based resources.
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TL;DR: Improvements and expansions to several branches of the Gene Ontology, as well as updates that have allowed us to more efficiently disseminate the GO and capture feedback from the research community are described.
Abstract: The Gene Ontology (GO; http://wwwgeneontologyorg) is a community-based bioinformatics resource that supplies information about gene product function using ontologies to represent biological knowledge Here we describe improvements and expansions to several branches of the ontology, as well as updates that have allowed us to more efficiently disseminate the GO and capture feedback from the research community The Gene Ontology Consortium (GOC) has expanded areas of the ontology such as cilia-related terms, cell-cycle terms and multicellular organism processes We have also implemented new tools for generating ontology terms based on a set of logical rules making use of templates, and we have made efforts to increase our use of logical definitions The GOC has a new and improved web site summarizing new developments and documentation, serving as a portal to GO data Users can perform GO enrichment analysis, and search the GO for terms, annotations to gene products, and associated metadata across multiple species using the all-new AmiGO 2 browser We encourage and welcome the input of the research community in all biological areas in our continued effort to improve the Gene Ontology
2,529 citations
TL;DR: All data manually curated by the MINT curators have been moved into the IntAct database at EMBL-EBI and are merged with the existing IntAct dataset.
Abstract: IntAct (freely available at http://www.ebi.ac.uk/intact) is an open-source, open data molecular interaction database populated by data either curated from the literature or from direct data depositions. IntAct has developed a sophisticated web-based curation tool, capable of supporting both IMEx- and MIMIx-level curation. This tool is now utilized by multiple additional curation teams, all of whom annotate data directly into the IntAct database. Members of the IntAct team supply appropriate levels of training, perform quality control on entries and take responsibility for long-term data maintenance. Recently, the MINT and IntAct databases decided to merge their separate efforts to make optimal use of limited developer resources and maximize the curation output. All data manually curated by the MINT curators have been moved into the IntAct database at EMBL-EBI and are merged with the existing IntAct dataset. Both IntAct and MINT are active contributors to the IMEx consortium (http://www.imexconsortium.org).
1,602 citations
01 Jan 2010
TL;DR: It is found that women over 50 are more likely to have a family history of diabetes, especially if they are obese, than women under the age of 50.
Abstract: Hypertension 66 (20.3%) 24 (24.2%) 30 (16.3%) NS Diabetes 20 (6.2%) 7 (7.1%) 10 (5.4%) NS Excess weight 78 (24%) 27 (27.3%) 44 (23.9%) NS Smokers 64 (19.7%) 17 (17.2%) 35 (19.0%) NS Age >50 years 137 (42.2%) 54 (54.5%) 67 (36.4%) <0.02 Kidney disease 7 (2.2%) 1 (1%) 5 (2.7%) NS Family history, DM 102 (31.4%) 28 (28.3%) 66 (35.9%) NS
1,369 citations
TL;DR: A common signaling mechanism used by all three types of innate immune receptor-adaptor protein pairs to activate IRF3 and generate IFNs is reported, which is important because cells must regulate their IFN production carefully to avoid inflammation and autoimmunity.
Abstract: During virus infection, the adaptor proteins MAVS and STING transduce signals from the cytosolic nucleic acid sensors RIG-I and cGAS, respectively, to induce type I interferons (IFNs) and other antiviral molecules. Here we show that MAVS and STING harbor two conserved serine and threonine clusters that are phosphorylated by the kinases IKK and/or TBK1 in response to stimulation. Phosphorylated MAVS and STING then bind to a positively charged surface of interferon regulatory factor 3 (IRF3) and thereby recruit IRF3 for its phosphorylation and activation by TBK1. We further show that TRIF, an adaptor protein in Toll-like receptor signaling, activates IRF3 through a similar phosphorylation-dependent mechanism. These results reveal that phosphorylation of innate adaptor proteins is an essential and conserved mechanism that selectively recruits IRF3 to activate the type I IFN pathway.
1,153 citations
TL;DR: MycoCosm is a fungal genomics portal developed by the US Department of Energy Joint Genome Institute to support integration, analysis and dissemination of fungal genome sequences and other 'omics' data by providing interactive web-based tools.
Abstract: MycoCosm is a fungal genomics portal (http://jgi.doe.gov/fungi), developed by the US Department of Energy Joint Genome Institute to support integration, analysis and dissemination of fungal genome sequences and other 'omics' data by providing interactive web-based tools. MycoCosm also promotes and facilitates user community participation through the nomination of new species of fungi for sequencing, and the annotation and analysis of resulting data. By efficiently filling gaps in the Fungal Tree of Life, MycoCosm will help address important problems associated with energy and the environment, taking advantage of growing fungal genomics resources.
1,037 citations
Cites methods from "Gene ontology annotations and resou..."
...Interpro, KEGG and Swiss-Prot hits are used to map gene ontology (GO) terms (21)....
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TL;DR: The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing.
Abstract: Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
35,225 citations
University at Buffalo1, University of Cambridge2, University of Washington3, University of Edinburgh4, Drexel University5, University of Utah6, European Bioinformatics Institute7, Lawrence Berkeley National Laboratory8, Bowling Green State University9, Massachusetts Institute of Technology10, University of Texas Southwestern Medical Center11, Stanford University12, University of Pennsylvania13
TL;DR: This work describes the OBO Foundry initiative and provides guidelines for those who might wish to become involved and describes an expanding family of ontologies designed to be interoperable and logically well formed and to incorporate accurate representations of biological reality.
Abstract: The value of any kind of data is greatly enhanced when it exists in a form that allows it to be integrated with other data. One approach to integration is through the annotation of multiple bodies of data using common controlled vocabularies or 'ontologies'. Unfortunately, the very success of this approach has led to a proliferation of ontologies, which itself creates obstacles to integration. The Open Biomedical Ontologies (OBO) consortium is pursuing a strategy to overcome this problem. Existing OBO ontologies, including the Gene Ontology, are undergoing coordinated reform, and new ontologies are being created on the basis of an evolving set of shared principles governing ontology development. The result is an expanding family of ontologies designed to be interoperable and logically well formed and to incorporate accurate representations of biological reality. We describe this OBO Foundry initiative and provide guidelines for those who might wish to become involved.
2,492 citations
01 Jan 2010
TL;DR: It is found that women over 50 are more likely to have a family history of diabetes, especially if they are obese, than women under the age of 50.
Abstract: Hypertension 66 (20.3%) 24 (24.2%) 30 (16.3%) NS Diabetes 20 (6.2%) 7 (7.1%) 10 (5.4%) NS Excess weight 78 (24%) 27 (27.3%) 44 (23.9%) NS Smokers 64 (19.7%) 17 (17.2%) 35 (19.0%) NS Age >50 years 137 (42.2%) 54 (54.5%) 67 (36.4%) <0.02 Kidney disease 7 (2.2%) 1 (1%) 5 (2.7%) NS Family history, DM 102 (31.4%) 28 (28.3%) 66 (35.9%) NS
1,369 citations
TL;DR: A dictionary of molecular entities focused on ‘small’ chemical compounds and an ontological classification, whereby the relationships between molecular entities or classes of entities and their parents and/or children are specified.
Abstract: Chemical Entities of Biological Interest (ChEBI) is a freely available dictionary of molecular entities focused on ‘small’ chemical compounds. The molecular entities in question are either natural products or synthetic products used to intervene in the processes of living organisms. Genome-encoded macromolecules (nucleic acids, proteins and peptides derived from proteins by cleavage) are not as a rule included in ChEBI. In addition to molecular entities, ChEBI contains groups (parts of molecular entities) and classes of entities. ChEBI includes an ontological classification, whereby the relationships between molecular entities or classes of entities and their parents and/or children are specified. ChEBI is available online at http://www.ebi.ac.uk/chebi/
1,006 citations
TL;DR: The OWL API is a high level Application Programming Interface (API) for working with OWL ontologies that supports parsing and rendering in the syntaxes defined in the W3C specification; manipulation of ontological structures; and the use of reasoning engines.
Abstract: We present the OWL API, a high level Application Programming Interface (API) for working with OWL ontologies. The OWL API is closely aligned with the OWL 2 structural specification. It supports parsing and rendering in the syntaxes defined in the W3C specification (Functional Syntax, RDF/XML, OWL/XML and the Manchester OWL Syntax); manipulation of ontological structures; and the use of reasoning engines. The reference implementation of the OWL API, written in Java, includes validators for the various OWL 2 profiles - OWL 2 QL, OWL 2 EL and OWL 2 RL. The OWL API has widespread usage in a variety of tools and applications.
792 citations
"Gene ontology annotations and resou..." refers background in this paper
...OWL is now a crucial component of the GO internal infrastructure, and provides many advantages to bioinformatics users of the GO, including standardized Application Programmer Interfaces (APIs) such as the OWLAPI (6), standardized means of persistent storage and querying in RDF triplestores, and fast, powerful reasoners....
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...org/ (6) Contact GOC at: go-helpdesk@ebi....
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