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A method for increasing expressivity of Gene Ontology annotations using a compositional approach
BACKGROUND: The 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....
Autores principales: | , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4039540/ https://www.ncbi.nlm.nih.gov/pubmed/24885854 http://dx.doi.org/10.1186/1471-2105-15-155 |
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author | Huntley, Rachael P Harris, Midori A Alam-Faruque, Yasmin Blake, Judith A Carbon, Seth Dietze, Heiko Dimmer, Emily C Foulger, Rebecca E Hill, David P Khodiyar, Varsha K Lock, Antonia Lomax, Jane Lovering, Ruth C Mutowo-Meullenet, Prudence Sawford, Tony Van Auken, Kimberly Wood, Valerie Mungall, Christopher J |
author_facet | Huntley, Rachael P Harris, Midori A Alam-Faruque, Yasmin Blake, Judith A Carbon, Seth Dietze, Heiko Dimmer, Emily C Foulger, Rebecca E Hill, David P Khodiyar, Varsha K Lock, Antonia Lomax, Jane Lovering, Ruth C Mutowo-Meullenet, Prudence Sawford, Tony Van Auken, Kimberly Wood, Valerie Mungall, Christopher J |
author_sort | Huntley, Rachael P |
collection | PubMed |
description | BACKGROUND: The 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. RESULTS: The 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. CONCLUSIONS: The 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. |
format | Online Article Text |
id | pubmed-4039540 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-40395402014-05-31 A method for increasing expressivity of Gene Ontology annotations using a compositional approach Huntley, Rachael P Harris, Midori A Alam-Faruque, Yasmin Blake, Judith A Carbon, Seth Dietze, Heiko Dimmer, Emily C Foulger, Rebecca E Hill, David P Khodiyar, Varsha K Lock, Antonia Lomax, Jane Lovering, Ruth C Mutowo-Meullenet, Prudence Sawford, Tony Van Auken, Kimberly Wood, Valerie Mungall, Christopher J BMC Bioinformatics Methodology Article BACKGROUND: The 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. RESULTS: The 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. CONCLUSIONS: The 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. BioMed Central 2014-05-21 /pmc/articles/PMC4039540/ /pubmed/24885854 http://dx.doi.org/10.1186/1471-2105-15-155 Text en Copyright © 2014 Huntley et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/4.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Methodology Article Huntley, Rachael P Harris, Midori A Alam-Faruque, Yasmin Blake, Judith A Carbon, Seth Dietze, Heiko Dimmer, Emily C Foulger, Rebecca E Hill, David P Khodiyar, Varsha K Lock, Antonia Lomax, Jane Lovering, Ruth C Mutowo-Meullenet, Prudence Sawford, Tony Van Auken, Kimberly Wood, Valerie Mungall, Christopher J A method for increasing expressivity of Gene Ontology annotations using a compositional approach |
title | A method for increasing expressivity of Gene Ontology annotations using a compositional approach |
title_full | A method for increasing expressivity of Gene Ontology annotations using a compositional approach |
title_fullStr | A method for increasing expressivity of Gene Ontology annotations using a compositional approach |
title_full_unstemmed | A method for increasing expressivity of Gene Ontology annotations using a compositional approach |
title_short | A method for increasing expressivity of Gene Ontology annotations using a compositional approach |
title_sort | method for increasing expressivity of gene ontology annotations using a compositional approach |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4039540/ https://www.ncbi.nlm.nih.gov/pubmed/24885854 http://dx.doi.org/10.1186/1471-2105-15-155 |
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