GOnet: a tool for interactive Gene Ontology analysis
Abstract Background Biological interpretation of gene/protein lists resulting from -omics experiments can be a complex task. A common approach consists of reviewing Gene Ontology (GO) annotations for entries in such lists and searching for enrichment patterns. Unfortunately, there is a gap between m...
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Format: | Article |
Language: | English |
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BMC
2018-12-01
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Series: | BMC Bioinformatics |
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Online Access: | http://link.springer.com/article/10.1186/s12859-018-2533-3 |
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author | Mikhail Pomaznoy Brendan Ha Bjoern Peters |
author_facet | Mikhail Pomaznoy Brendan Ha Bjoern Peters |
author_sort | Mikhail Pomaznoy |
collection | DOAJ |
description | Abstract Background Biological interpretation of gene/protein lists resulting from -omics experiments can be a complex task. A common approach consists of reviewing Gene Ontology (GO) annotations for entries in such lists and searching for enrichment patterns. Unfortunately, there is a gap between machine-readable output of GO software and its human-interpretable form. This gap can be bridged by allowing users to simultaneously visualize and interact with term-term and gene-term relationships. Results We created the open-source GOnet web-application (available at http://tools.dice-database.org/GOnet/), which takes a list of gene or protein entries from human or mouse data and performs GO term annotation analysis (mapping of provided entries to GO subsets) or GO term enrichment analysis (scanning for GO categories overrepresented in the input list). The application is capable of producing parsable data formats and importantly, interactive visualizations of the GO analysis results. The interactive results allow exploration of genes and GO terms as a graph that depicts the natural hierarchy of the terms and retains relationships between terms and genes/proteins. As a result, GOnet provides insight into the functional interconnection of the submitted entries. Conclusions The application can be used for GO analysis of any biological data sources resulting in gene/protein lists. It can be helpful for experimentalists as well as computational biologists working on biological interpretation of -omics data resulting in such lists. |
first_indexed | 2024-12-11T05:57:28Z |
format | Article |
id | doaj.art-7c6e358ab7524df2bb840811bb661712 |
institution | Directory Open Access Journal |
issn | 1471-2105 |
language | English |
last_indexed | 2024-12-11T05:57:28Z |
publishDate | 2018-12-01 |
publisher | BMC |
record_format | Article |
series | BMC Bioinformatics |
spelling | doaj.art-7c6e358ab7524df2bb840811bb6617122022-12-22T01:18:38ZengBMCBMC Bioinformatics1471-21052018-12-011911810.1186/s12859-018-2533-3GOnet: a tool for interactive Gene Ontology analysisMikhail Pomaznoy0Brendan Ha1Bjoern Peters2Department of Vaccine Discovery, La Jolla Institute for Allergy and ImmunologyDepartment of Vaccine Discovery, La Jolla Institute for Allergy and ImmunologyDepartment of Vaccine Discovery, La Jolla Institute for Allergy and ImmunologyAbstract Background Biological interpretation of gene/protein lists resulting from -omics experiments can be a complex task. A common approach consists of reviewing Gene Ontology (GO) annotations for entries in such lists and searching for enrichment patterns. Unfortunately, there is a gap between machine-readable output of GO software and its human-interpretable form. This gap can be bridged by allowing users to simultaneously visualize and interact with term-term and gene-term relationships. Results We created the open-source GOnet web-application (available at http://tools.dice-database.org/GOnet/), which takes a list of gene or protein entries from human or mouse data and performs GO term annotation analysis (mapping of provided entries to GO subsets) or GO term enrichment analysis (scanning for GO categories overrepresented in the input list). The application is capable of producing parsable data formats and importantly, interactive visualizations of the GO analysis results. The interactive results allow exploration of genes and GO terms as a graph that depicts the natural hierarchy of the terms and retains relationships between terms and genes/proteins. As a result, GOnet provides insight into the functional interconnection of the submitted entries. Conclusions The application can be used for GO analysis of any biological data sources resulting in gene/protein lists. It can be helpful for experimentalists as well as computational biologists working on biological interpretation of -omics data resulting in such lists.http://link.springer.com/article/10.1186/s12859-018-2533-3Gene ontologyGSEAInteractiveWeb-appGenomicsProteomics |
spellingShingle | Mikhail Pomaznoy Brendan Ha Bjoern Peters GOnet: a tool for interactive Gene Ontology analysis BMC Bioinformatics Gene ontology GSEA Interactive Web-app Genomics Proteomics |
title | GOnet: a tool for interactive Gene Ontology analysis |
title_full | GOnet: a tool for interactive Gene Ontology analysis |
title_fullStr | GOnet: a tool for interactive Gene Ontology analysis |
title_full_unstemmed | GOnet: a tool for interactive Gene Ontology analysis |
title_short | GOnet: a tool for interactive Gene Ontology analysis |
title_sort | gonet a tool for interactive gene ontology analysis |
topic | Gene ontology GSEA Interactive Web-app Genomics Proteomics |
url | http://link.springer.com/article/10.1186/s12859-018-2533-3 |
work_keys_str_mv | AT mikhailpomaznoy gonetatoolforinteractivegeneontologyanalysis AT brendanha gonetatoolforinteractivegeneontologyanalysis AT bjoernpeters gonetatoolforinteractivegeneontologyanalysis |