Dataset Information

The Gene Ontology knowledgebase in 2023.

ABSTRACT: 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.

SUBMITTER: Gene Ontology Consortium

PROVIDER: S-EPMC10158837 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature

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The Gene Ontology knowledgebase in 2023.

Aleksander Suzi A SA Balhoff James J Carbon Seth S Cherry J Michael JM Drabkin Harold J HJ Ebert Dustin D Feuermann Marc M Gaudet Pascale P Harris Nomi L NL Hill David P DP Lee Raymond R Mi Huaiyu H Moxon Sierra S Mungall Christopher J CJ Muruganugan Anushya A Mushayahama Tremayne T Sternberg Paul W PW Thomas Paul D PD Van Auken Kimberly K Ramsey Jolene J Siegele Deborah A DA Chisholm Rex L RL Fey Petra P Aspromonte Maria Cristina MC Nugnes Maria Victoria MV Quaglia Federica F Tosatto Silvio S Giglio Michelle M Nadendla Suvarna S Antonazzo Giulia G Attrill Helen H Dos Santos Gil G Marygold Steven S Strelets Victor V Tabone Christopher J CJ Thurmond Jim J Zhou Pinglei P Ahmed Saadullah H SH Asanitthong Praoparn P Luna Buitrago Diana D Erdol Meltem N MN Gage Matthew C MC Ali Kadhum Mohamed M Li Kan Yan Chloe KYC Long Miao M Michalak Aleksandra A Pesala Angeline A Pritazahra Armalya A Saverimuttu Shirin C C SCC Su Renzhi R Thurlow Kate E KE Lovering Ruth C RC Logie Colin C Oliferenko Snezhana S Blake Judith J Christie Karen K Corbani Lori L Dolan Mary E ME Drabkin Harold J HJ Hill David P DP Ni Li L Sitnikov Dmitry D Smith Cynthia C Cuzick Alayne A Seager James J Cooper Laurel L Elser Justin J Jaiswal Pankaj P Gupta Parul P Jaiswal Pankaj P Naithani Sushma S Lera-Ramirez Manuel M Rutherford Kim K Wood Valerie V De Pons Jeffrey L JL Dwinell Melinda R MR Hayman G Thomas GT Kaldunski Mary L ML Kwitek Anne E AE Laulederkind Stanley J F SJF Tutaj Marek A MA Vedi Mahima M Wang Shur-Jen SJ D'Eustachio Peter P Aimo Lucila L Axelsen Kristian K Bridge Alan A Hyka-Nouspikel Nevila N Morgat Anne A Aleksander Suzi A SA Cherry J Michael JM Engel Stacia R SR Karra Kalpana K Miyasato Stuart R SR Nash Robert S RS Skrzypek Marek S MS Weng Shuai S Wong Edith D ED Bakker Erika E Berardini Tanya Z TZ Reiser Leonore L Auchincloss Andrea A Axelsen Kristian K Argoud-Puy Ghislaine G Blatter Marie-Claude MC Boutet Emmanuel E Breuza Lionel L Bridge Alan A Casals-Casas Cristina C Coudert Elisabeth E Estreicher Anne A Livia Famiglietti Maria M Feuermann Marc M Gos Arnaud A Gruaz-Gumowski Nadine N Hulo Chantal C Hyka-Nouspikel Nevila N Jungo Florence F Le Mercier Philippe P Lieberherr Damien D Masson Patrick P Morgat Anne A Pedruzzi Ivo I Pourcel Lucille L Poux Sylvain S Rivoire Catherine C Sundaram Shyamala S Bateman Alex A Bowler-Barnett Emily E Bye-A-Jee Hema H Denny Paul P Ignatchenko Alexandr A Ishtiaq Rizwan R Lock Antonia A Lussi Yvonne Y Magrane Michele M Martin Maria J MJ Orchard Sandra S Raposo Pedro P Speretta Elena E Tyagi Nidhi N Warner Kate K Zaru Rossana R Diehl Alexander D AD Lee Raymond R Chan Juancarlos J Diamantakis Stavros S Raciti Daniela D Zarowiecki Magdalena M Fisher Malcolm M James-Zorn Christina C Ponferrada Virgilio V Zorn Aaron A Ramachandran Sridhar S Ruzicka Leyla L Westerfield Monte M

Genetics 20230501 1

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, internati ...[more]

PMID: 36866529

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Project description:MotivationOntologies such as the Gene Ontology (GO) and their use in annotations make cross species comparisons of genes possible, along with a wide range of other analytical activities. The bio-ontologies community, in particular the Open Biomedical Ontologies (OBO) community, have provided many other ontologies and an increasingly large volume of annotations of gene products that can be exploited in query and analysis. As many annotations with different ontologies centre upon gene products, there is a possibility to explore gene products through multiple ontological perspectives at the same time. Questions could be asked that link a gene product's function, process, cellular location, phenotype and disease. Current tools, such as AmiGO, allow exploration of genes based on their GO annotations, but not through multiple ontological perspectives. In addition, the semantics of these ontology's representations should be able to, through automated reasoning, afford richer query opportunities of the gene product annotations than is currently possible.ResultsTo do this multi-perspective, richer querying of gene product annotations, we have created the Logical Gene Ontology, or GOAL ontology, in OWL that combines the Gene Ontology, Human Disease Ontology and the Mammalian Phenotype Ontology, together with classes that represent the annotations with these ontologies for mouse gene products. Each mouse gene product is represented as a class, with the appropriate relationships to the GO aspects, phenotype and disease with which it has been annotated. We then use defined classes to query these protein classes through automated reasoning, and to build a complex hierarchy of gene products. We have presented this through a Web interface that allows arbitrary queries to be constructed and the results displayed.ConclusionThis standard use of OWL affords a rich interaction with Gene Ontology, Human Disease Ontology and Mammalian Phenotype Ontology annotations for the mouse, to give a fine partitioning of the gene products in the GOAL ontology. OWL in combination with automated reasoning can be effectively used to query across ontologies to ask biologically rich questions. We have demonstrated that automated reasoning can be used to deliver practical on-line querying support for the ontology annotations available for the mouse.AvailabilityThe GOAL Web page is to be found at http://owl.cs.manchester.ac.uk/goal.

Project description:The Gene Ontology Annotation (GOA) database (http://www.ebi.ac.uk/GOA) aims to provide high-quality electronic and manual annotations to the UniProt Knowledgebase (Swiss-Prot, TrEMBL and PIR-PSD) using the standardized vocabulary of the Gene Ontology (GO). As a supplementary archive of GO annotation, GOA promotes a high level of integration of the knowledge represented in UniProt with other databases. This is achieved by converting UniProt annotation into a recognized computational format. GOA provides annotated entries for nearly 60,000 species (GOA-SPTr) and is the largest and most comprehensive open-source contributor of annotations to the GO Consortium annotation effort. By integrating GO annotations from other model organism groups, GOA consolidates specialized knowledge and expertise to ensure the data remain a key reference for up-to-date biological information. Furthermore, the GOA database fully endorses the Human Proteomics Initiative by prioritizing the annotation of proteins likely to benefit human health and disease. In addition to a non-redundant set of annotations to the human proteome (GOA-Human) and monthly releases of its GO annotation for all species (GOA-SPTr), a series of GO mapping files and specific cross-references in other databases are also regularly distributed. GOA can be queried through a simple user-friendly web interface or downloaded in a parsable format via the EBI and GO FTP websites. The GOA data set can be used to enhance the annotation of particular model organism or gene expression data sets, although increasingly it has been used to evaluate GO predictions generated from text mining or protein interaction experiments. In 2004, the GOA team will build on its success and will continue to supplement the functional annotation of UniProt and work towards enhancing the ability of scientists to access all available biological information. Researchers wishing to query or contribute to the GOA project are encouraged to email: goa@ebi.ac.uk.

Project description:Endometriosis is a complex, benign, estrogen-dependent gynecological disorder with an incidence of ~10% women in reproductive age. The implantation and growth of endometrial cells outside the uterus leads to the development of endometriosis. Endometriosis is also associated with comorbid conditions like cardiovascular and autoimmune diseases. The absence of non-invasive diagnostic markers, delayed diagnosis, high risk of recurrence of the disease on surgical removal of the tissue and absence of a definitive cure for endometriosis makes it imperative to gain insights into the complex etiology of endometriosis. A plethora of genes identified from blood and endometrial biopsies, involved in different pathways like steroid metabolism, angiogenesis, inflammation, etc. have been associated with endometriosis. However, the exact mechanism and genetic etiology of endometriosis still remain unclear. The polygenic nature of the disease, incongruent phenotypic manifestations in different ethnic populations and information scattered in literature makes it difficult to delineate the sub-network of genes that will aid in disease diagnosis and effective treatment. Endometriosis Knowledgebase is a manually curated database with information on genes associated with endometriosis. It holds information on 831 genes, their associated polymorphisms, gene ontologys, pathways and diseases. Genes in the database are enriched in pathways important for cell signaling, immune regulation and reproduction. A genetic overlap is seen between endometriosis and cancers, endocrine/reproductive, nervous system, immune and metabolic diseases. Network analysis of genes in the Endometriosis Knowledgebase helped predict 13 new candidate genes for endometriosis. These genes were found to be enriched in biological processes associated with endometriosis. The Endometriosis Knowledgebase and incorporated tools for gene and sequence-based analysis will benefit both researchers and clinicians working in the realm of reproductive biology.

Dataset Information

The Gene Ontology knowledgebase in 2023.

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The Gene Ontology knowledgebase in 2023.

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