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CvManGO, a method for leveraging computational predictions to improve literature-based Gene Ontology annotations
The set of annotations at the Saccharomyces Genome Database (SGD) that classifies the cellular function of S. cerevisiae gene products using Gene Ontology (GO) terms has become an important resource for facilitating experimental analysis. In addition to capturing and summarizing experimental results...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3308158/ https://www.ncbi.nlm.nih.gov/pubmed/22434836 http://dx.doi.org/10.1093/database/bas001 |
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author | Park, Julie Costanzo, Maria C. Balakrishnan, Rama Cherry, J. Michael Hong, Eurie L. |
author_facet | Park, Julie Costanzo, Maria C. Balakrishnan, Rama Cherry, J. Michael Hong, Eurie L. |
author_sort | Park, Julie |
collection | PubMed |
description | The set of annotations at the Saccharomyces Genome Database (SGD) that classifies the cellular function of S. cerevisiae gene products using Gene Ontology (GO) terms has become an important resource for facilitating experimental analysis. In addition to capturing and summarizing experimental results, the structured nature of GO annotations allows for functional comparison across organisms as well as propagation of functional predictions between related gene products. Due to their relevance to many areas of research, ensuring the accuracy and quality of these annotations is a priority at SGD. GO annotations are assigned either manually, by biocurators extracting experimental evidence from the scientific literature, or through automated methods that leverage computational algorithms to predict functional information. Here, we discuss the relationship between literature-based and computationally predicted GO annotations in SGD and extend a strategy whereby comparison of these two types of annotation identifies genes whose annotations need review. Our method, CvManGO (Computational versus Manual GO annotations), pairs literature-based GO annotations with computational GO predictions and evaluates the relationship of the two terms within GO, looking for instances of discrepancy. We found that this method will identify genes that require annotation updates, taking an important step towards finding ways to prioritize literature review. Additionally, we explored factors that may influence the effectiveness of CvManGO in identifying relevant gene targets to find in particular those genes that are missing literature-supported annotations, but our survey found that there are no immediately identifiable criteria by which one could enrich for these under-annotated genes. Finally, we discuss possible ways to improve this strategy, and the applicability of this method to other projects that use the GO for curation. Database URL: http://www.yeastgenome.org |
format | Online Article Text |
id | pubmed-3308158 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-33081582012-03-20 CvManGO, a method for leveraging computational predictions to improve literature-based Gene Ontology annotations Park, Julie Costanzo, Maria C. Balakrishnan, Rama Cherry, J. Michael Hong, Eurie L. Database (Oxford) Original Articles The set of annotations at the Saccharomyces Genome Database (SGD) that classifies the cellular function of S. cerevisiae gene products using Gene Ontology (GO) terms has become an important resource for facilitating experimental analysis. In addition to capturing and summarizing experimental results, the structured nature of GO annotations allows for functional comparison across organisms as well as propagation of functional predictions between related gene products. Due to their relevance to many areas of research, ensuring the accuracy and quality of these annotations is a priority at SGD. GO annotations are assigned either manually, by biocurators extracting experimental evidence from the scientific literature, or through automated methods that leverage computational algorithms to predict functional information. Here, we discuss the relationship between literature-based and computationally predicted GO annotations in SGD and extend a strategy whereby comparison of these two types of annotation identifies genes whose annotations need review. Our method, CvManGO (Computational versus Manual GO annotations), pairs literature-based GO annotations with computational GO predictions and evaluates the relationship of the two terms within GO, looking for instances of discrepancy. We found that this method will identify genes that require annotation updates, taking an important step towards finding ways to prioritize literature review. Additionally, we explored factors that may influence the effectiveness of CvManGO in identifying relevant gene targets to find in particular those genes that are missing literature-supported annotations, but our survey found that there are no immediately identifiable criteria by which one could enrich for these under-annotated genes. Finally, we discuss possible ways to improve this strategy, and the applicability of this method to other projects that use the GO for curation. Database URL: http://www.yeastgenome.org Oxford University Press 2012-02-13 /pmc/articles/PMC3308158/ /pubmed/22434836 http://dx.doi.org/10.1093/database/bas001 Text en © The Author(s) 2012. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/3.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Articles Park, Julie Costanzo, Maria C. Balakrishnan, Rama Cherry, J. Michael Hong, Eurie L. CvManGO, a method for leveraging computational predictions to improve literature-based Gene Ontology annotations |
title | CvManGO, a method for leveraging computational predictions to improve literature-based Gene Ontology annotations |
title_full | CvManGO, a method for leveraging computational predictions to improve literature-based Gene Ontology annotations |
title_fullStr | CvManGO, a method for leveraging computational predictions to improve literature-based Gene Ontology annotations |
title_full_unstemmed | CvManGO, a method for leveraging computational predictions to improve literature-based Gene Ontology annotations |
title_short | CvManGO, a method for leveraging computational predictions to improve literature-based Gene Ontology annotations |
title_sort | cvmango, a method for leveraging computational predictions to improve literature-based gene ontology annotations |
topic | Original Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3308158/ https://www.ncbi.nlm.nih.gov/pubmed/22434836 http://dx.doi.org/10.1093/database/bas001 |
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